Abstract
Biosensors and nanoscale analytical tools have shown huge growth in literature in the past 20 years, with a large number of reports on the topic of ‘ultrasensitive’, ‘cost-effective’, and ‘early detection’ tools with a potential of ‘mass-production’ cited on the web of science. Yet none of these tools are commercially available in the market or practically viable for mass production and use in pandemic diseases such as coronavirus disease 2019 (COVID-19). In this context, we review the technological challenges and opportunities of current bio/chemical sensors and analytical tools by critically analyzing the bottlenecks which have hindered the implementation of advanced sensing technologies in pandemic diseases. We also describe in brief COVID-19 by comparing it with other pandemic strains such as that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) for the identification of features that enable biosensing. Moreover, we discuss visualization and characterization tools that can potentially be used not only for sensing applications but also to assist in speeding up the drug discovery and vaccine development process. Furthermore, we discuss the emerging monitoring mechanism, namely wastewater-based epidemiology, for early warning of the outbreak, focusing on sensors for rapid and on-site analysis of SARS-CoV2 in sewage. To conclude, we provide holistic insights into challenges associated with the quick translation of sensing technologies, policies, ethical issues, technology adoption, and an overall outlook of the role of the sensing technologies in pandemics.
Keywords: nanosensors, nanoplasmonics, sewage sensors, microfluidics, atomic force microscopy, X-ray diffraction, electron microscopy, point-of-care-technologies, COVID-19, pandemics
In the last 15 years, humans have witnessed 5 pandemic diseases, severe acute respiratory syndrome (SARS), Swine flu, Ebola, Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19), where both MERS and COVID-19 are actively present within our community, with the latter having more severe complications and infection rates in recent times. COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus, which was actively revealed in Wuhan, China in December 2019 and later sequenced in January 2020.1,2 In just a period of 2 months (March 2020), the disease had spread to 90% of the countries on our planet. In parallel, there remains a great interest in the biosensors and nanoscale visualization/characterization tools such as the electron microscopy (EM), atomic force microscopy (AFM), and X-ray diffraction (XRD) for the early detection and revealing prognostic features of a given pathogen.3,4 Recent advances have also extended the use of these advanced sensing techniques into the development of high-throughput devices capable of screening a large number of candidate compounds for quick drug discovery and vaccine development during pandemics.5−7 Most of the latest sensors currently available in both academia and industry are based on the electrical or optical transduction methods.8 These include single-molecule sensors,9 wearable devices,10 ingestible capsules,11 disposable screen-printed electrodes,12 and hand-held portable instruments13 which could actively detect the infectious disease-causing pathogen at an early stage of the infection. Nevertheless, despite their vast availability and well-known advantages in academia, the full potential of the biosensing and characterization tools is yet to be harnessed in on-site applications, such as during the outbreak of an infectious disease. Certainly, there are scientific challenges and opportunities for bio/chemical sensors before they can live up to the expectation of accurate, precise, and early diagnosis of emerging diseases. It is in this context that we present an analysis of the state of art instruments and technologies, in particular concerning COVID-19, for the detection and analysis of pandemic strains. In general, we can segregate the detection of viruses into three main categories: (1) direct detection of the virus, (2) viral RNA/DNA detection, and (3) antibody detection.
These sensing strategies are further complemented by the aforementioned surface visualization/characterization tools (AFM, EM, XRD) presenting a complete package of technologies that can potentially be used for diagnosis, prognosis, drug discovery, drug analysis, and disease spread surveillance, all of which are vital for ensuring good community health.14,15 A general overview of these groups including their mechanisms is discussed in Table 1. It is also obvious that for quick technology development, such as for the development of biosensors for COVID-19, it is essential to build on the existing scientific evidence for the use of technology in similar applications. Therefore, we begin by highlighting similarities and differences between other RNA viral infections from the perspective of understanding the COVID-19 disease and subsequent technology development to serve the required demand for sensing and characterization of the SARS-CoV2 virus.
Table 1. Categorization of Biosensing Strategies and Surface Characterization Techniques for Virus Detection.
applications |
||||||
---|---|---|---|---|---|---|
mechanism | diagnosis | prognosis | drug discovery | drug analysis | ||
direct detection of virus | complete virus is detected individually using biosensors or more commonly by Petri dish-based cell culture techniques18,19 | X | X | – | – | |
viral RNA/DNA detection | RT-PCR, PCR principles applied either in conventional nucleic acid formats (with fluorescence or radioactive labels) or using advanced techniques such as LSPR, SPR, QCM, and other potentiometric sensing techniques20−22 | X | X | X | – | |
antibody/antigen detection | microwell-based bioassays using absorbance readers and several optical and electronic biosensors which essentially measure the binding kinetics of the biomolecule of interest23 | – | X | X | X | |
surface characterization tools as complementary sensing techniques | AFM | high-resolution scanning probe microscopy with a resolution 1000 times smaller than the optical diffraction limit used for investigating surface properties of viruses in the order of fraction of a nanometer24,25 | – | X | X | X |
EM | the surface of a virus is imaged with a focused beam of electrons to identify topographical features26 | – | X | X | X | |
XRD | crystallographic features of virus are mapped to determine 3D structures of the viruses27,28 | – | – | X | X |
‘X’ denotes suitability of application and ‘–’ indicates application not applicable. In the application column, we refer to diagnosis as a quick detection either for in use in hospital or self-testing. Note that visualization tools can still be used for diagnosis but are deemed unsuitable for quick detection.
Similarity of SARS-CoV-2 with Other Viral Strains
COVID-19 disease is highly contiguous as its transmission is encouraged by airborne droplets and touch-based human to human contact.16 Common symptoms of this disease are similar to influenza, and therefore, it is not trivial to rapidly detect the disease accurately. Comparative analysis of the SARS-CoV2 with other epidemic viral strains serves as background information on which researchers can build upon to create potential technologies to quickly address the demands of disease control in an emergency.15 While there are several similarities and differences between these viruses, here we focus only on those features which will help researchers working in the field of biosensors and surface visualization/characterization to develop strategies for accurate detection and morphological analysis of SARS-CoV2. One striking similarity between SARS-CoV2 virus and influenza, H1NI, SARS-CoV, and MERS viruses is that all contain RNA as their genetic material, therefore also known as RNA viruses. Compared to DNA viruses, RNA viruses are technically more virulent as they infect cells by injecting RNA which quickly transcribes and replicates viral proteins in the host cell.17 This also makes it extremely challenging to detect a RNA virus at an early stage of the infection. Cell culture and nucleic acid-based tests have been the gold standard since their inception as compared to all other techniques.18
However, unlike the DNA viruses which have polymerase machinery, the nucleic acid tests for RNA viruses include additional steps of reverse translation of RNA to DNA before it is amplified.30 Structurally, as viruses responsible for COVID-19, SARS, and MERS outbreaks belong to the same category of beta coronavirus family, these viruses are mostly spherical, but sometimes pleomorphic in shape.31,32 The average diameter of these viruses is 125 nm, where their capsid covering the genetic material is around 85 nm and glycoprotein spikes on the surface of the capsid are roughly 20 nm;33 see more out of scale structural features of SARS-CoV2 virus in Figure 1. Comparing genetic characteristics of the SARS-CoV2 virus with other viruses may also provide useful insights to develop sensing strategies for the early detection of COVID-19. For example, untranslated regions (UTR) of the RNA, both at 5 prime and 3 prime ends are involved in the binding of the cellular proteins.34 The detailed arrangement of coding regions of coronaviruses is given in Figure 2. In addition, the mechanism of how coronaviruses infect cells can also be useful for developing sensors for analyzing the disease mechanism and drug discovery. In brief, the SARS-CoV2 virus enters a given cell via protein–protein interactions where the glycoprotein spikes bind to the angiotensin-converting enzyme 2 (ACE2) on the cell surface.34 This attachment of the virus on the cell and further entry into the cell is assisted by protease enzyme called TMPRSS2. After the entry of the SARS-CoV2 virus into the cells, the RNA quickly translates into proteins, including the RNA synthesis in the cytoplasm by viral replication.31
Figure 1.
Structural view of SARS-CoV2 virus and its surface protein. Reproduced with permission from ref (29) under a Creative Commons CC-BY license. Copyright 2020 StatPearls Publishing.
Figure 2.
5’ UTR and 3’ UTR and coding region of COVID-19, SARS-CoV and MERS-CoV. Reprinted with permission from ref (34). Copyright 2020 Elsevier.
Current Detection and Tracing Technologies for COVID-19
Current detection for COVID-19 is primarily based on the combination of two or more techniques which include RT-PCR, chest X-ray, computed tomography (CT) scans, and the detection of some common biomarkers in the blood.35 These biomarker tests include identification of elevated levels of the C-reactive protein, low procalcitonin, low lymphocyte counts, and high concentration of interleukin 6 and interleukin 10. Details of reverse transcriptase polymerase chain reaction (RT-PCR), CT scans, biochemical assays, and the development of mobile phone-based digital contact tracing applications are discussed in the proceeding subsections.
Molecular Method
Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is a method used to detect the presence of nucleic acid-based genetic sequences from any organism, including viruses.36,37 To perform RT-qPCR for SARS-CoV2, first the biological fluid where the virus strains are present, upper and lower respiratory fluid, is collected. The collection of fluid is generally performed using nasopharyngeal and oropharyngeal swabs.38 Then the collected fluid undergoes a number of filtration and separation steps to isolate the viral RNA. Using the reverse transcriptase enzyme, complementary viral DNA (cDNA) is generated from the viral RNA. Specific regions of cDNA then undergo a polymerase chain reaction for amplification, where an additional DNA probe, designed to hybridize within a small part of the specific region of cDNA, is incorporated to enable real-time detection of the amplification process. Traditionally, radioactive isotopes were used as markers to target the specific nucleic acids, but more recently, fluorescence tags (fluorophore and a quencher) are used on the DNA probe for the real-time detection.39 Essentially when the DNA polymerase enzyme is adding nucleotides to the specific part of the viral cDNA, it encounters the double-stranded DNA in its path (due to DNA probe) where the exonuclease activity of the polymerase enzyme separates the fluorophore and the quencher molecules to produce real-time detection of the viral cDNA. If the number of cDNA copies (proportional to the concentration of the virus) produced after transcription is high, a large amount of fluorescence signal is generated after few rounds of polymerase reaction, and if the system is calibrated well, the fluorescence intensity is directly proportional to the concentration of virus in the infected patients. The typical sensitivity that can be achieved by RT-PCR is between 500 and 1000 copies/mL of viral RNA. Among the current tests, three regions of the cDNA have been identified for the detection of the SARS-CoV2 virus, which are the RdRP, E, and N genes. More details about these sequences are described in Table 1 of the work from Udugama et al.38 and on the Center for Disease Control and Prevention website.40 One challenge that remains is also that SARS-CoV2 damages the target RNA when opening its viral capsid. One reason might be due to the immune response of the host body. This leads to a release of small fragments of RNA into the bloodstream which is challenging for detection by RT-PCR. Perhaps, signal enrichment strategies for isolation of RNA fragments using advanced technologies such as CRISPR or in combination with nanomaterials such as gold nanoparticles and metal–organic complexes may provide a solution to this problem. Another reason for poor sensitivity using RT-PCR is that the RNA easily gets degraded and typically requires an immediate frozen storage condition, and the completion of handling samples may also induce the poor detection. However, the point-of-use sensors, discussed later, may provide an immediate testing of RNA and will overcome this issue from the samples.
Computer Tomography
Computer tomography, which is more popular with the name of CT scan, is being used either in combination or as a standalone diagnostic tool to confirm the false hits from RT-PCR for the detection of SARS-CoV2 virus.41−43 In this method, usually a large number of X-ray measurements of the chest are taken from different angles to generate three-dimensional (3D) images with contrast, which are later analyzed by radiologists for abnormal features to authenticate the presence of SARS-CoV2 infection. Signatures of COVID-19 infection which appear in CT scan include areas of subpleural regions of ground glass opacification affecting the lower parts of either a single lobe or both lobes.44 In addition, data from COVID-19 patients have shown consolidation of fluids in the lungs. Moreover, the CT images have so far also assisted in serving as a prognostic tool for COVID-19. For example, in the early days (0–2 days) of the infection, the CT scan resembles more of a normal chest condition. As the disease progresses, the opacity of the scan increases, for instance, patients after 4 days of the infection have been reported with ground glass opacity which even appears as irregular patterns in the scan. Figure 3(45) shows a CT scan image from different patients on different days where the various abnormalities are clearly visible such as focal ground glass opacity and include bilateral and peripheral predominant consolidation after 20 days of the onset of the disease. While CT scans are likely to remain as one of the most important tools in the early diagnosis of the COVID-19, the major challenge for the radiologists lies in the distinction of symptoms from other lung disorders or pneumonia-like symptoms which are not due to COVID-19 infections. In fact, the main caveat of using CT scans for COVID-19 is that the specificity of the detection is around 25%, as reported in the literature so far,44,46 as the imaging features overlap with other viral pneumonia. In addition, CT scans are expensive and require advanced technical skills for the operation and analysis, and it can only be adapted as a complementary technology with RT-PCR for SARS-CoV2 detection.
Figure 3.
Transverse thin-section CT scans in patients with COVID-19 disease: (A) 56 year-old man, day 3 after symptom onset; (B) 74 year-old woman, day 10 after symptom onset; (C) 61 year-old woman, day 20 after symptom onset; and (D) 63 year-old woman, day 17 after symptom onset. Reprinted with permission from ref (45). Copyright 2020 Elsevier.
Biochemical Tests
Biochemical tests such as the enzyme-linked immunosorbent assay (ELISA) have also been used to detect the viral protein or the antibodies that are created by our body in response to a SARS-CoV2 infection for the diagnosis of COVID-19 disease. The standard procedure to detect these antibodies involves the use of microtiter plates such as the 96-well, where antibodies are detected via protein–protein interactions. These interactions are amplified using a fluorescence, luminescence, or colorimetric type of detection assisted by enzymes involved in the reaction. However, one main issue is that as the amount of viral load changes during the course of the infection, so detecting low concentrations of viral protein might be difficult. For instance, To et al.47 showed salivary viral loads in the first week after the onset of symptoms, which gradually decreased with time. On the other hand, detecting antibodies generated in response to the viral infection is more specific for confirming presence of the COVID-19 disease. Nevertheless, there are still potential challenges with developing accurate serological tests such as those related with the cross-reactivity of SARS-CoV2 antibodies with other antibodies generated against coronaviruses.48 There are still some important advantages of the antibody testing, for instance, the presence of antibodies can verify if vaccines are functioning as intended.49 It can also be used in disease contact tracing applications, weeks after an individual is infected with the virus. Perhaps currently it is the most important test to help inform the intervention policymakers on how many asymptomatic cases exist in a given population.50 However, false negative results from the current antibody tests are often reported which are attributed to the combination of following technical reasons: (1) a low concentration of antibodies typically present in fluidic samples; (2) presence of homologous proteins; and (3) lack of sensitivity from the detection instrument. While, low concentration of antibodies and the presence of homologous proteins will remain challenging for the biosensing community, the sensitivity of the current tools could perhaps be enhanced by development of an engineered protein which will attach specifically to the antibody of interest. However, the ultimate solution is the development of ultrasensitive and selective biosensors, which we discuss more in the proceeding sections.
Contact Tracing
Use of digital technologies, especially smartphone-based applications to identify the presence of infected cases, both active and recovered, has also been implemented successfully to manage the spread of COVID-19.51 Ferrite et al.52 recently developed an application for contact tracing, see more in Figure 4. In this application, the main idea is to replace a manual contact tracing with instantaneous transmission of signals to and from a central server. Essentially, the SARS-CoV2 virus cases are directly communicated to the server, which enables recommendation of risk-stratified quarantine and social distancing measures in those now known to be possible contacts. This is done by preserving absolute anonymity of the infected person. In addition, tests can also be requested by symptomatic individuals through the developed application. One of the pros of such software-based technology is that it can easily be tuned to be more informative, for example, quarantining areas if the disease spread in that area becomes uncontrolled, quarantining specific flats or families, or performing second or third degree contact tracing if number of cases rise. Another advantage of contact tracing is the accessibility of technology with members of the community. In the past few years there has been a steady rise in the adoption of smartphones and mobile internet which could potentially facilitate a coordinated response during pandemics. One main limitation of contact tracing is that to harness the full potential of the technology, it is also necessary to increase the number of diagnostic tests. Another limitation such as privacy consideration also exists, which with changing time and circumstances are becoming more acceptable within the community members.53 The use of contact tracing however is still useful for members of a community to track the movement of people who came in contact with the infected, including the asymptomatic ones.
Figure 4.
Contact tracing application: Using GPS contacts of individual A and all individuals using the app, infections are traced out. This is further supplemented by scanning QR-codes displayed on high-traffic public amenities where GPS is too coarse. Using this application individual A requests a test for COVID-19 infection, and their positive test result is shared as an instant notification to individuals who have been in close contact. Reproduced with permission under a Creative Commons CC-BY license from ref (52). Copyright 2020 American Association for the Advancement of Science.
Characteristics of COVID-19 Disease for Developing Strategies for Analysis
Physical Characteristics of SARS-CoV2 Virus
There are four major structural proteins in SARS-CoV2 virus: E (envelope protein), M (membrane protein), N (nucleocapsid protein), and S (spike protein).54,55 Each protein not only individually plays a role in the virus structure but also is involved in the mechanism of the replication cycle.56 The smallest and most enigmatic major proteins of CoV viruses is the E protein. During the replication cycle, although a small portion of E protein is incorporated into the virion envelope, it is abundantly expressed inside the infected cell.57 The primary function of the N protein is to bind to the CoV RNA genome and to form the structure of the nucleocapsid.55 In addition, of its major role in processes of viral genome, it involves the CoV replication cycle and also the host cellular response to viral infection.58 The shape of the viral envelop is defined by the M protein.59 The other function of M protein is its role as the central organizer in CoV assembly through interactions with other major structural proteins.60 Coronavirus enters into host cells by the attachment of transmembrane spike (S) glycoprotein to the host surface receptor. Also, the S protein mediates the subsequent fusion between the viral and host cell membranes to make the entry of viral cell easier into the host cell.57,61 The S protein binds to ACE2, a transmembrane receptor which is widely expressed in the lungs, kidneys, gastrointestinal tissue, and heart.62 The binding to the host cell receptor and fusion between viral and cellular membranes have been performed with two subunits of the S protein: S1 subunit and S2 subunit, respectively.63 The S2 subunit contains neutralizing epitopes of the virus, including a conserved fusion peptide, a transmembrane domain, a cytoplasmic domain, and heptad repeats 1 and 2.64 In the S1 subunit, the core receptor binding domains are highly conserved. The differences in the amino acids are the cause of the direct interaction between spike protein and host cell receptor.65
Potential Small Molecule Biomarkers in Blood
A range of small molecule biomarkers in the blood has recently been identified for the COVID-19 detection from patients all around the globe. While many of these biomarkers are not as specific as detecting the viral RNA or DNA directly from the blood or by using RT-PCR, their concentration in the blood is found to be useful for the prognostics of the COVID-19 disease. This is due to the fact that many protein-based molecules in the blood, which eventually serve as the biomarkers of a disease, increase or decrease their levels in order to fight against the infection. Therefore, some of these biomarkers are directly linked to the severity of the infection. Particularly, serum urea, (CREA), C (CysC) serum direct bilirubin (DBIL), (CHE), and lactate dehydrogenase (LDH) concentrations were found to be significantly higher in severe COVID-19 patients than those in mild COVID-19 patients.66−68 More recently, smell dysfunction has been proposed as a biomarker for the COVID-19 disease.69 The mucous secretions from the nose contain many protein molecules that help metabolize xenobiotics and support epithelial integrity required for smell function. Therefore, developing biosensors for the detection of mucous proteins may also be useful for the early detection of the SARS-CoV2 infection. However, it should be noted that these biomarkers may not be specific to COVID-19 as nasal dysfunction is reported in other diseases such as the Parkinson’s disorder.70 More qualitative and quantitative information regarding the trend of biomarkers for COVID-19 is summarized in Table 2. This certainly provides first-hand information for the designer of a biosensor to develop strategy for selecting either one or multiple candidates to develop test platforms for precise and accurate detection of the COVID-19 disease.
Table 2. Potential Blood Biomarkers of SARS-CoV-2.
biomarker | reference range | concentration range in COVID-19 patients | ref |
---|---|---|---|
C-reactive protein (CRP) | 0.068–8.2 mg/L | >15 mg/L | (66) |
procalcitonin | 0–0.5 ng/mL | <0.1 ng/mL | (66) |
lymphocyte count | 0.8 to 4 × 109/L | <0.6 × 109/L | (66) |
interleukin-6 (IL-6) | 0–7 pg/mL | >15 pg/mL | (66) |
interleukin-10 (IL-10) | 0–9.1 pg/mL | >15 pg/mL | (67) |
cystatin C (CysC) | 0.6–1.3 mg/dl | >1.1 mg/dl | (68) |
serum direct bilirubin (DBIL) | 5.1–17 μmol/L | >8–60 μmol/L | (68) |
cholinesterase (CHE) | 8–18 kU/L | 1.5–8 kU/L | (68) |
lactate dehydrogenase (LDH) | 140–280 U/L | 300–600 U/L | (68) |
creatinine (CREA) | 60–120 μmol/L | 30–80 μmol/L | (68) |
urea | 2.5–7.1 mmol/L | 4–22 mmol/L | (68) |
Qualitative changes in the blood profile of COVID-19 patientsa | |||
hematologic | WBC count | increases | |
neutrophil count | |||
lymphocyte count | decreases | ||
platelet count | |||
eosinophil count | |||
hemoglobin | |||
biochemical | alanine aminotransferase | increases | |
aspartate aminotransferase | |||
total bilirubin | |||
blood urea nitrogen | |||
creatinine | |||
creatine kinase | |||
lactate dehydrogenase | |||
myoglobin | |||
creatine kinase-MB | |||
cardiac troponin I | |||
albumin | decreases | ||
coagulation | prothrombin time | increases | |
D-dimer | |||
inflammatory biomarkers | erythrocyte sedimentation rate | increases | |
CRP | |||
serum-ferritin | |||
PCT | |||
IL-2R | |||
IL-6 | |||
IL-8 | |||
IL-10 |
The information is adapted from the clinical studies of Henry et al.75
Respiratory Burst
Essentially, polymorphonuclear leukocytes (PMN) circulating in our blood show elevated levels in the blood as a defense response of the host during an episode of infection. PMN inherit different pieces of information in the case of different etiological agents stimulating immunity.71,72 This causes activation of PMN, and there is an increase in the consumption of molecular oxygen which results in the production of reactive oxygen species (ROS), a process collectively called respiratory burst.73 Some biosensors in the past have been developed for the ROS detection from the virus, and on a similar basis identification of ROS from SARS-CoV2 infection may lead to an alternative route of COVID-19 detection.74
Biosensors for Early Detection and Prognosis of Pandemic Viral Strains
There are 11 key attributes (Figure 5):
High selectivity: Selectivity of a biosensor is its ability to exclusively detect the analyte in the presence of other homologous analytes and contaminants. Viruses usually depict similarity in their structures in that they have a nucleic acid genome, a protein capsid which covers the genome, and in some cases lipid covering the genome and a protein coat.76 This protein coat in the virus distinguishes them from the bacteria.77 Perhaps specifically identifying and then targeting some proteins on the capsid with other proteins via protein–protein interactions may lead to selective detection of the virus.78−80 A good selectivity is usually achieved by ensuring that the monolayer of probes that target the chosen biomarkers on the analyte is optimally immobilized on the sensor surface.81,82 Now this is most often a subject of research where it usually takes a dedicated team and a time frame of 6–12 months to develop probes specific to the target. This obviously is a bottleneck for quick development of a biosensor during pandemics such as the COVID-19. However, the advancements in nanotechnology and the pace with which discovery and invention of materials are developing present a good opportunity for researchers to develop chemical probes, specific only for the target under detection.83,84 While developing specific probes is obviously a challenge, recent work has demonstrated an antifouling coating for the electrodes consisting of biomolecules mixed in a network of conductive nanomaterials.85 These layers were found to preserve 88% of the original detection signals for IL-6 even after 1 month of exposure to unprocessed human plasma.
High sensitivity: The spacing, affinity, and specificity of the target probes in the monolayer, often called self-assembled monolayers (SAM) or biorecognition elements, determine the attachment of the analyte on the sensor surface.86,87 In addition, properties of the transducer (most often electrical or optical) influence the overall sensitivity of the biosensor;88 for instance, how many molecules should be attached before a response from the sensor could easily be distinguished from its inherent noise.89 If specificity of the SAM layer is ensured, then it is possible to detect several small biomarkers (sizes <150 kDa) even at the single molecular level.90,91 For the detection of biomarkers associated with pandemic strains such as the COVID-19, it is of the utmost importance that the developed sensor can detect the disease-specific biomarkers at a low concentration, preferably at a single molecule level, and produce an output which is easily readable for the measured/required concentration. For instance, one major challenge, if addressed, to detect the viral DNA or RNA directly in whole blood without the need of amplification via PCR technology would lead to technologies for rapid detection of the pandemic viral strains. Some complementary tools such as the nanoparticle-based detection tools92,93 and microfluidics94,95 might also be well-placed to address some of these fundamental issues involving biological fluid handling on the sensor surface to enhance sensitivity.96
Rapid response time: For a diagnosis tool to be actively used in a pandemic, the response time of the sensor is of paramount importance.97 Theoretically, most transducers in the sensor respond instantaneously (<1 s) to the applied stimulus, such as upon the interaction of biomolecule with the sensor surface.98 However, at times, many of these signals require post-processing with advanced electronics and computing systems for accurate analysis of the measured quantity. For instance, temperature correction99 and background noise identification and removal100 can often lead to an increase in the sensor response time. Therefore, the design and role of signal conditional circuits are of prime importance to ensure quick response time of the measurement.
Multiplexing: At early stages of the infection when there is less understanding of the characteristics of the viral strain, it is often the concentrations of common blood biomarkers (such as CRP and interlukien as described for COVID-19) which serve as the signature of the infection. As also described earlier that for COVID-19 detection, a combination of more than two biomarkers is currently used to confirm the disease; therefore, a multiplexed system which allows detection of multiple biomarkers is desirable for a quick, accurate, and early detection of the disease. Multiplexing can be achieved by physically isolating different areas of the sensor surface, where each isolated area acts as a standalone sensor.101,102 Each of these individual areas could further be made specific to one type of biomarker.103−105 Measurement can be acquired by either one transducer which will scan the isolated areas of the sensor surface or by incorporation of multiple transducers attached to the individual sensing area.106,107
Multimode sensing for on-chip validation: For the detection of a pandemic strain, it is also necessary that the sensor, while providing a quick response, is also reliable. To increase the reliability in the measurement of the sensor, more than one mode of sensing could provide cross validation of the sensing result.107 The major trade-off of the multimode sensing is an increase in the size of physical dimensions and computation time of the sensor. This can also lead to a slow response time, increased power consumption, and cost ineffectiveness. However, the good news is that most of these barriers are rapidly being addressed. Electrochemical and optical techniques on single platforms108 which can easily be translated into multiplexed panels are increasingly getting attention in both academia and industry, which might be able to serve the purpose of increased reliability and robustness required in the healthcare sensors, by instant authentication of the measured data.
Disposable: The fact that the pandemic viral strains are highly infectious, for example, COVID-19 has a reproductive number higher than that established for SARS and H1N1, that is, between 1.5 and 2, the need for single-use sensors is crucial for avoiding contamination from the sensing systems. The most judicial pathway to develop a disposable sensor system is the modular approach.109−111 In this approach, electrode and readout modules can be separately designed where electrodes can be made cost-effective and disposable in nature. Candidate materials for developing disposable electrodes could potentially include glass-,112 paper-,113,114 plastic-,115,116 metal-,117,118 or ceramic-based119 materials which can be used to immobilize bioprobes specific to the biomolecules of our interest. Among these materials, paper-based biosensing electrodes,112,113 which have grabbed the attention of the community in recent times, provide the most optimal disposable features, as such electrodes could be classified into burnable trash after use. On the other hand, readout modules in the form of a mobile phone application-based120 readout will provide many advantages other than cost-effectiveness, such as periodic data collection and connectivity to the centralized healthcare systems.
Long shelf life and easy to use: The electrodes developed should be easy to use, and their lifetime should be at least 1 month. This will ensure that a large number of testing kits can be prepared and shipped not only to the healthcare facilities but also to the supermarket shelves.121,122 The ease of use could allow people to self-test and take necessary and informed decisions to self-isolate, which will ensure that the transmission of the disease can be curtailed at its source.
Cost effective: The advantage of cost-effectiveness of a biosensor is effectively reflected in its affordability.123 Intuitively, the lower the price of a given biosensor, the more affordable is the device. To keep the price low, so that all members of the community can afford it during pandemics, a biosensing system can essentially be divided into two parts: The first part can be a disposable electrode such as the screen printed electrode or a paper-based electrode, which could potentially be available in the supermarket drug shelves.124,125 The electrode could have direct contact with a body fluid such as saliva for the quick detection of viral strain. The second part of the sensor system can be an application on a mobile phone which can essentially serve the purpose of a direct readout of signals from the sensor.126,127 Such applications could potentially be made available by the government or healthcare government body of the affected region or community. On the other hand, a standalone readout and data acquisition system with data loggers for disposable sensors can also be developed, which could be equipped in hospitals or at border control posts of a country.128 Certainly there are some biosensors that are now easily available in the supermarkets such as the pregnancy test strips and lateral flow strips for sexually transmitted disorders and for glucose meters.125 It is on similar lines that sensors which can rapidly detect viral infection, if developed, could turn out to very useful for self-testing of infectious diseases. In addition, this would decrease the load on the public health bodies to detect the disease in a large-sized population, which could also lead to timely control of the disease spread.
Mass manufacturing: During pandemics, there is an urgent and large demand for sensors that can detect the fast spreading disease accurately and quickly. Due to lack of availability and reachability of a number of sensors, low rates of testing compared to total population are reported in the case of COVID-19, even in the nations with world leading heath infrastructures.129,130 In an ideal situation, the number of biosensors is equal to the population number of a given geographical area under test to ensure that all potential members of the community that can spread the disease are identified at an early stage. While there is a significant technological challenge to mass-produce sensors, the latest advancements in the manufacturing such as the 3D printing131 and machine molding132 can help in the development of a large number of sensors in a very short duration of time.
Autonomy and connectivity to central healthcare systems: Autonomy in the biosensing system would ensure a high compatibility between electrodes and the readout modules. Along with autonomy, measurement systems of a sensor in pandemics should be able to connect itself with the central database of a hospital which will collect real-time data from the measurements. For instance, a mobile phone application which collects the data from the sensor can be integrated with a two-way communication channel to (1) send data to the central database and (2) provide prompt therapeutic intervention or facilitate the situation with dispatched paramedical staff.133−135 Networked healthcare service can also maintain and troubleshoot by additional information about the functionality of a sensor. Furthermore, location, positive disease cases, personal details such as age, gender, and contact information could easily be logged on to the central databases. This would provide the government and health care policy markers with real-time data to quickly and accurately determine necessary actions, such as locking down certain locations to contain and mitigate the fast spread of the disease.52
Figure 5.
Features of an ideal biosensor required to be developed for effective use in pandemics.
Among the 11 key attributes discussed above, the most important focus of current biosensing innovations should be the sensitivity and specificity of the assays directed toward early detection of COVID-19 disease or future pandemic strains. This is necessary to overcome the uncertainty associated with a wide variety of advanced testing technology including lateral flow assays or direct detection of viral RNA with CRISPR biosensors.136 Repurposing some of the instrumentation such as the 96-well microtiter plate readers could also potentially provide multiple replicates of a bioassay in a short duration of time. Such absorbance readers are so common that nearly all laboratories which provide routine blood profile testing, including in the remote settings, may utilize this platform for quick testing. For instance, absorbance readers are used in recent work on the quick detection of COVID-19 disease,137 suggesting that the capital required for new schemes developed already exists within many test facilities. Another important aspect which could potentially lead to a quick solution required in mid-pandemics is the use of soft-biosensing. For instance, artificially intelligent software could be installed in hand-held thermometers which are currently being widely used in the screening of COVID-19 candidate patients. Software could perhaps be developed in such a way that the infrared signals analyzed by the detector provide information on some key skin biomarkers, in addition to the temperature measurement, similar to non-invasive biosensing.138−140
Electronic Sensors for COVID-19
Field-effect-transistors (FETs) and three-electrode potentiometric and amperometric systems are the key electronic biosensors widely applicable in the detection of biomolecules and pathogens. The main advantages of an electronic biosensor are miniaturization, low cost, and mass manufacturing. Besides, the concepts of a modular sensor for separating electrode and readout on a smartphone can be implemented effectively. FETs for instance are easily fabricated in CMOS foundaries,141−143 whereas a large number of electrochemical sensors are now commercially available in the market in portable formats.144,145 In the past a large number of electrochemical sensors have been developed for pandemic viral strains. For instance, Han et al. developed a single microfluidic electrochemical sensor for the detection of H1N1, H5N1, and H7N9 combined with zinc oxide nanorods,146 amplification strategies for sensitive detection of H1N1 with electrochemical sensors by Li et al.,147 electrochemical detection of MERS using carbon electrodes by Layqah et al.,148 detection of SARS by Iskhikawa et al.,149 and many other works which even used disposable screen printed electrodes and paper-based substrates for the detection of viral strains.150−152 Some of these works have definitely encouraged researchers to develop strategies for the early detection of COVID-19 disease. The work of Seo et al. reported a FET biosensor for detecting SARS-CoV2 in clinical samples.153 This sensor was produced by coating the gate of the transistor made up of graphene sheets, with an antibody that was specific against the SARS-CoV2 spike protein. Desired performance of the sensor was identified with tests conducted with antigen as protein, cultured virus, and nasal swab specimens from COVID-19 patients. The biosensor could detect the SARS-CoV2 spike protein at concentrations of 1 fg/mL in buffer solvents prepared in the laboratory and 100 fg/mL from the biological fluid of the clinical samples. This biosensor was further used for the successful detection of viral strains in culture medium with a limit of detection in clinical samples found around 2.42 × 102 copies/mL. This was indeed a neat example of how biosensors can detect the virus at low concentration without sample pretreatment or labeling, see more details in Figure 6. In another recent work, Mahari et al. developed a biosensor using three electrode-electrochemical systems by using disposable screen-printed carbon electrodes.154 The limit of detection of this biosensor named, eCovSens, was found to be 120 fM in buffer solvents. Even though the detection was performed in nonclinical samples, the electrodes were found stable for up to 4 weeks, suggesting that such electrodes could potentially be mass-produced, shipped, and then distributed in the community within a reasonable time frame during the mid-pandemics.
Figure 6.
Detection of SARS-CoV2 using FETs: The schematic shows a collection of biological samples from a patient and its application on the graphene-based sensing area of the FET biosensor. Binding events associated with the SAR-CoV2 virus can be captured by the sensor in real time. Reprinted with permission from ref (153). Copyright 2020 American Chemical Society.
Optical Biosensors for COVID-19
A large number of optical biosensors primarily based on the principle of plasmonics,155 essentially where the transduction principles use optical components such as waveguides,156 fiber optics,157 photonic crystals,158 and lasers159 are classified into optical sensors. Optical biosensors such as the surface plasmon sensors (SPR), including the localized surface plasmon resonance (LSPR), are commercially available since early 1990s,160 and they have been widely used to detect viral strains such as those associated with H1N1,161 SARS,162 MERS,163 and influenza164 in laboratory settings.
While many of the developed plasmonic techniques with advanced surface chemistry provide high sensitivity, selectivity, and quick response time for the detection of the viral strains, their use in point-of-care applications remains challenging. This is due to the large size and cost of the instrumentation which is involved in the development of plasmonic systems. Even though the use of these sensors remains elusive for the mass production and self-testing by community members, these sensors are now well-developed for widespread use in laboratory-based settings for accurate detection of viral strains during pandemics. Recently, Qiu et al. have developed a biosensor for accurate diagnosis of COVID-19 disease where plasmonic photothermal (PPT) effect and LSPR are combined as transduction principles in the sensing scheme.165 Essentially, the DNA receptors are used to detect selected sequences from the SARS-CoV2 through nucleic acid hybridization. With the mandatory use of nanoparticles and light in LSPR sensing, there is a generation of well-known thermoplasmonic at the plasmonic resonance frequency. The authors claim to elevate the in situ hybridization temperature using this thermoplasmonic heat which further assists in facilitating accurate discrimination of two similar gene sequences. This LSPR biosensor showed a good limit of detection, down to the concentration of 0.22 pM, exhibiting a high sensitivity toward the selected SARS-CoV-2 sequences in a multigene mixture; see more details in Figure 7.
Figure 7.
LSPR detection of nucleic acid sequences from SARS-CoV2 virus. The schematic shows the architecture of LSPR substrate consisting of gold nanoparticles where light is illuminated on the substrate for generation of local heat and detection of binding nucleic acid binding events. The graph also shows the LSPR response to the theroplasmonic effect and toward detection of nucleic acid sequences at low concentrations. Reprinted with permission from ref (165). Copyright 2020 American Chemical Society.
It should be noted that with current advancements in the nanofabrication166 and combination of LSPR with microfluidics,167 further enhancement of sensitivity and potential integration in portable formats is feasible.168,169 Therefore, LSPR holds a great potential to improve the diagnostic accuracy in the clinical tests and relieve the pressure on PCR-based tests that usually need a well-equipped laboratory and well-trained personnel, which usually remains challenging due to a shortage of resources during pandemics.
In addition to the direct sensing of analytes which confirm viral infection, optical sensors can also play a significant role in high-resolution imaging of the viruses during pandemics. For example, Wei et al. demonstrated how optical sensors can be used for virus imaging based on the laser diode excitation.170 The developed system is in the form of an optomechanical attachment which can easily be integrated to the existing camera module of a smart phone, see more details in Figure 8. The special resolution of the imaging platform is <50 nm, allowing easy detection of individual viruses using a smartphone.
Figure 8.
Imaging single viruses using a smartphone. A smartphone-based optomechanical attachment with a resolution of <50 nm for the detection of individual viruses. Reprinted with permission from ref (170). Copyright 2013 American Chemical Society.
Biosensors for Drug Analysis and Accelerating Drug Discovery for Vaccines
As biosensors provide detailed information on the binding affinity of the biomolecules to a given substrate and their associated kinetics, their use in drug discovery applications is ubiquitous. In addition, as the design of a given transducer can be tuned according to its intended application, different types of biosensors can be implemented in various levels of the drug discovery process to enhance the discovery of a potential vaccine.171 In addition, as viruses evolve,172,173 the need for a new drug is also inevitable, and it is in this context where sensing technologies can provide a solid backbone to bear the burden of a timely discovery of new drugs to contain an outbreak of a disease. In the drug discovery process, the research and development phase of the drug discovery process normally takes 3–5 years, preclinical studies take 1–2 years, and clinical trials take 2–3 years, followed by review and approvals which take over 6 months to 1 year.174 Time of these stages can significantly be reduced with the use of advanced bio/chemical sensing technologies. We provide key attributes of biosensors specific to the drug discovery stages that can possibly assist in speeding up the drug discovery process:
Ligand fishing: Identification of potential drugs from natural resources such as the plant extracts is a critical, challenging, and time-consuming task in the process of drug discovery.175 For example, discovery of a bioactive constituent from traditional Indian and Chinese medicines has contributed to their well-known therapeutic effects in viral diseases, including for SARS CoV2-virus.176−178 Identification of such bioactive compounds has largely been dependent on techniques such as ultrafiltration,179 liquid chromatography (LC),180 and mass spectrometry (MS),181 known as ligand fishing. It is in this context that all biosensors represent themselves as a useful tool for the discovery of bioactive compounds from a complex mixture of natural products while providing enhanced efficiency and reduced sample preparation time at a low cost as compared to the existing techniques such as the LS and MS. While biosensors combined with the nanomaterials have also shown high selectivity toward the targets, sensors for screening active compounds from natural products have not been reported so far. However, by combining biosensors with microfluidics, it is possible to develop such tools due to common procedures such as incubation,182 washing,183 mixing,184 and compartmentalization185 of the solution being easily achieved by microfluidics systems.
High-throughput screening of candidate drug compounds: In contrast to the extraction of compounds from the natural products, in laboratory synthesis of chemical compounds for possible use as drugs, the ability of the compound to affect the identified target using biosensors can be tested. Here high-throughput array-based sensing technologies such as the FETs186−188 surface plasmon sensors189,190 have an advantage over the conventional techniques of LS and MS where effectively more than 10,000 compounds can be screened in 1 week as compared to 3–4 years of testing. In addition to compound screening, cross-reactive activity of the candidate compounds,191 to check their interfere with other homologous targets, could also be tested using such sensing platforms. An example of a high-throughput biosensor chip consisting 9000 electrodes is shown in Figure 9.
Affinity and kinetics: Affinity and kinetics information on a compound is vital in understanding its use as a potential drug.192 For instance, biosensors can closely be made to perform experiments based on the bioinformatics modeling algorithms.193 In preclinical trials, sensing platforms can assist in evaluating the binding affinity and kinetics of the antibodies or to assess the developed vaccine to the target protein or loci on the virus. Statistically relevant information on affinity and kinetics can also be generated in a short duration of time with use of multiplexed biosensing arrays.194
Monitoring chemical parameters in the pharmaceutical production process: The production of drugs operates in a stringent safety, sterility, and precision-oriented environment. Therefore, use of biosensors in monitoring chemical parameters such as pH, temperature, and enzymatic activity in real time during the process of drug production is significant to improve the quality of the drugs.195 Other bio/chemical sensors which measure gases such as oxygen and concentration of byproducts in the process are also useful in the identification of physical and chemical errors in the production line of the drugs.196 A significant challenge and opportunity for some of the advanced biosensors is to essentially integrate themselves in the production line in the form of a sensor network. Further, with integration of machine learning programs, data from these biosensors could potentially be used to improve the delivery time of drugs in large quantities required during pandemics.197
Bedside drug monitoring in hospitals: Another significant opportunity for biosensors in pandemics is in drug analysis by biomedical systems equipped along the bedside in the hospitals.198,199 An appropriate quantity of drug injected in the body of the patient can significantly improve the recovery time of the patient. Most often, it is essential to monitor various parameters of the body such as the blood pressure, heart rate, temperature, oxygen concentration, and other small molecules in the blood profile.200 Establishing a real-time relationship between concentrations of aforementioned parameters is essential, and this is where biosensors, if combined with the internet of technology, can effectively be used for the measurement and analysis of drugs in real time.
Figure 9.
High-throughput biosensor: This example shows (A) microelectrode array used for cell detection; (B) sensor layout and the addressing scheme employed in the CMOS sensor chip; (C) complete CMOS packaged biochip; and D) microphotograph showing 9000 electrodes in the chip. Reprinted with permission from ref (188). Copyright 2012 Elsevier.
Microfluidic Biosensors for Pandemics
Microfluidics is a technique for precise control and manipulation of microscale fluids.201 The basic operation units such as preparation, extraction, reaction, and detection in various analysis processes are integrated on a microchip. Through the micromachining process, the micro- to submillimeter-level fluid channels, pumps, valves, sensors, detectors, and other units can be fabricated on the substrate of silicon, metal, polymer, or other materials. So far, microfluidic platforms have been applied using various analytical techniques including electrochemical analysis, fluorescence analysis, MS, and chemiluminescence.202 Microfluidic platforms can be classified into capillary, pressure-driven, centrifugal, electrokinetic, and acoustic systems on the basis of their liquid propulsion principles.203 In the past decades, microfluidic biosensors have been developed for the detection of infectious diseases in the medical field. Infectious diseases are usually identified by bacteria (e.g., Streptococcus agalactiae, Escherichia coli, Haemophilus influenza), food borne pathogens (e.g., salmonella, listeria, cholera toxin), and viruses (e.g., hepatitis C, influenza, dengue virus).204 For example, shear horizontal surface acoustic wave biosensors were established for multiplex detection of anti-p24 and anti-gp41 antibodies of HIV.205 The designed dual-channel biochip consists of a sensing area sensitive to biological binding events, along with in situ reference control coating and miniaturization configuration. The microfluidic biosensor can detect HIV biomarkers within 5 min using only 6 μL of plasma sample, which raises the prospects of the next generation of economical, rapid, and point-of-care diagnosis to prevent HIV pandemics. A silicon nanowire-based microfluidic biosensor was also developed to detect the RT-PCR product of dengue serotype 2.206 A detection limit as low as 10 fM was reported within 30 min, which was 3 orders of magnitude lower than that obtained by using 32P-labeled probes and 4 orders of magnitude lower than that achieved by ethidium bromide staining. During the COVID-19 pandemic, rapid diagnosis is of great significance in early diagnosis of SARS-CoV2 for timely and appropriate treatment. Although culture-based methods and molecular techniques are commonly used in clinical diagnosis, recently introduced microfluidic biosensors have the advantages of cheapness, portability, rapid response, high precision, high reproducibility, low reagent/sample consumption, easy application, and high-throughput parallel processing.207 Therefore, microfluidic biosensors can meet the World Health Organization guidelines “ASSURED” (e.g., affordable, specific, sensitive, user-friendly, rapid and robust, equipment free, and deliverable to end user),208 which have tremendous potential to be implemented for SARS-CoV2 detection in point-of-care COVID-19 diagnosis. However, further efforts should be devoted to improving sensitivity and specificity, strengthening stability and efficiency, and shortening fabrication time and analysis time for monitoring pandemics. It is also necessary to reduce the matrix effects of actual samples. Moreover, it remains challenging to develop fully automated microfluidic biosensors for on-site monitoring of pandemics.
Wastewater Biosensors for Early Warning of Outbreak
Wastewater-based epidemiology (WBE) has been implemented for the determination of various biomarkers, including illicit and licit drugs, pharmaceuticals, and personal care products, markers of population size, industrial chemicals, and biologicals.209,210 Human viruses (e.g., astroviruses, enteroviruses, noroviruses, rotaviruses, salivirus) have also been detected in wastewater, indicating that WBE holds great potential in the early determination of viral outbreaks by routinely monitoring the concentration and diversity of viruses in wastewater.211 Generally, WBE can provide information about viruses on a population scale and assess the temporal and spatial trends of virus occurrence in the watershed within the catchment of sewage treatment plants. The outbreak of COVID-19 caused by SARS-CoV2 has brought about a significant risk to human health. It has been suggested that SARS-CoV2 can be detected in fecal samples of confirmed COVID-19 cases from many countries including China,211,212 United States,213 Germany,214 Singapore,215 and Korea.216 The research involving 10 pediatric COVID-19 confirmed cases provided the potential evidence for fecal viral shedding of SARS-CoV2.217 Even if the results of the nasopharyngeal testing became negative, the rectal testing of eight children was still positive, indicating that viral shedding of gastrointestinal tract and fecal–oral transmission might occur. The presence of SARS-CoV2 in fecal samples may increase the virus load on the wastewater systems of infected cities.
Recent evidence suggests that determination of SARS-CoV2 in wastewater is helpful to study the virus transmission in humans since SARS-CoV2 can be excreted in feces or urine. Several attempts have been made to detect SARS-CoV2 in wastewater including the Netherlands,218 United States,219 France,220 Australia,221 Spain,222 and Sweden. The report of SARS- CoV2 detection in sewage was published by Gertjan Medema et al.218 in the Netherlands. Specifically, the targets of gene N (N1–N3) and gene E were tested by RT-qPCR in the WWTP sewage samples to determine the presence of SARS-CoV2. Neither gene N nor gene E was detected on February 6, while gene N1 was detected in six sites, and genes N3 and E were detected in five and four sites on March 15 and 16, respectively. This is consistent with the first case in the Netherlands reported on February 27. In the United States, SARS-CoV2 at high titers was determined by RT-PCR in wastewater samples from a major urban treatment facility in Massachusetts.217,218 Their work demonstrated that longitudinal analysis of wastewater can provide an estimate of the population level of SARS-CoV2 burden without available on-site testing. The appearance of SARS-CoV2 RNA was verified in six WWTP wastewater samples from a low prevalence area Murcia in Spain.222 Compared to the COVID-19 cases announced by the municipality, the environmental surveillance results clarified that SARS-CoV2 had spread among the population before the initial cases were reported in many cities. Therefore, WBE could be used as an early warning tool for the presence and prevalence of COVID-19 infections and provide a more accurate estimate of the spread extent of COVID-19 infections than clinical testing.223 RT-PCR assays have been routinely employed for SARS-CoV2 detection in numerous research and disease control centers since the outbreak of COVID-19,223 as also discussed in our aforementioned sections. However, RT-PCR has some disadvantages which may limit its applications globally. For example, complete facilities, expensive equipment, skilled technicians, and laborious procedures are essential for RT-PCR. With the rapid growth of COVID-19 confirmed cases worldwide, this method has been unable to meet the requirements of detecting numerous suspicious cases in a short time even though in some cases, PCR has demonstrated high sensitivity and specificity. Essentially, the requirements for complicated sample handling in the laboratory, skilled personnel, and a long period of data processing and analysis (4–6 h) are not conducive to real-time and effective monitoring of samples on location. Therefore, it is critical to develop efficient transportable and robust analytical tools to accurately and quickly trace low-level SARS-CoV2 sources through WBE to confirm these suspected cases and screen asymptomatic infected cases without centralized laboratories. Using a WBE strategy in creating an early warning and intervention system will require a rapid biosensing method for the on-site detection of viruses at the wastewater collection outlets.209,212 We have recently proposed the use of a paper-based device which can be used for WBE, as developed in our previous work,224 see Figure 10 for more details. Recent advances on signal amplification strategies have also lead to enhanced sensitivity in paper-based analytical devices for various sensing applications.225 Due to the advantages of low cost, rapidness, sensitivity, specificity, accuracy, and simplicity, biosensors have been applied in detecting different analytes for clinical diagnosis, food safety, and environmental monitoring. Recently, biosensors have been employed for electrochemical, optical, electrical, thermal, acoustic, and piezoelectric determination of infectious diseases such as AIDS, dengue, Ebola, influenza, and malaria. The biomarkers of these infectious diseases are usually identified in blood, feces, plasma, serum, sputum, saliva, urine and nasal mucosa. When detecting wastewater samples, the matrix effects of the wastewater can also be minimized using an ultrahigh-affinity probe for specific targets.226 Moreover, biosensors can be extended for the multiplex detection of infectious diseases. For instance, Men et al.227 presented a multifunctional fluorescent protein nanowire (FNw) for multiplex detection of hemagglutinin 1 protein from the influenza virus along with p24 and gp120 protein from HIV. The FNw integrated with numerous green fluorescent protein molecules could amplify signal and enhance sensitivity. In addition, biosensors can be developed into integrated medical devices for point-of-care detection of infectious diseases in resource-limited regions. For example, Choi et al.228 presented an integrated paper-based sample-to-answer biosensor for nucleic acid testing. The fast technology analysis (FTA) card and glass fiber were integrated into a lateral flow strip for nucleic acid extraction, amplification, eye detection, and smartphone quantification. A hand-held battery-powered heating device was also used for LAMP amplification in POC settings. Hence, biosensors can be employed as early warning sensing systems to help the government take effective measures in advance to prevent the spread of infectious diseases.
Figure 10.
Design of an integrated sensor for the detection of multiplex infectious disease pathogens. (a) Components of the sensor and (b) illustration of the complete sample processing from sample introduction to pathogen detection. Reprinted with permission from ref (224). Copyright 2018 American Chemical Society.
Biosensors hold potentials for in situ quantitative analysis of SARS-CoV2 in wastewater. Although wastewater is a complex matrix, we have recently proposed a paper-based device which has shown great potential to detect pathogens in wastewater. Essentially, we developed a fast “sample-to-answer” analysis method which can provide quantitative monitoring of nucleic acids and genetic information through the analysis of sewage waste.229 Our results were further cross validated with a robust electrophoresis and agarose gel image assay, showing promising reliability for wastewater analysis. The proposed biosensors will show advantages including affordability, rapid analysis time, excellent sensitivity, superior specificity, and low reagent/sample consumption. These benefits highlight the biosensors’ extraordinary capability of economical, portable, and user-friendly detection of SARS-CoV2 in wastewater compared to traditional detection methods. Biosensors will help identify COVID-19 infected patients on the spot to provide timely medical advice and monitor the spread of SARS-CoV2 at early stage. Therefore, sewage sensors for SARS-CoV2 detection at the population level have a clear potential for early warning of COVID-19 pandemic. For example, a recent study demonstrated that live SARS-CoV2 was isolated from the feces and urine of infected people, which would then enter the wastewater treatment system.230 Although SARS-COV-2 is usually present in sewage in the form of RNA particles, there currently is no evidence which shows live SARS-CoV-2 present in sewage. Within this viewpoint, further research on the environment for the analysis of disease biomarkers is required to effectively use WBE biosensors for an informed status of the disease within a community.
Nanoscale Visualization and Characterization Tools
There are three principal techniques commonly used individually or in combination for the direct visualization, diagnosis, and characterization of virus structure: XRD analysis, EM, and AFM. The exceptional capabilities of these instruments allow not only in-depth analysis of virus structure and function but also investigation of viral impact on host cells and extracellular environment, useful in drug discovery applications.28,231−241
Electron Microscopy
An EM is a tool that uses a beam of accelerated electrons as a source of illumination. Due to the short wavelength of electrons in comparison with visible light photons (up to 100,000 times), the resolving power of EM is higher than optical microscopes and can visualize the structure of particles at nanometer scales. The capability of EM to reveal the structures of nanoparticles makes it a powerful instrument for detection, diagnosis, and analysis of viruses (as viruses are of the same scale as that of many inorganic nanoparticles such as gold, extensively used in research). In comparison, with the molecular and serological approaches used for virus diagnosis which require specific probe to recognize virus, EM methods do not need organism-specific reagents for recognizing the pathogenic agent. It means for the case of an unknown disease, molecular tests need information about the potential agents to determine the appropriate tests, while EM provides an open view of whatever might be present in a given sample under investigation.237 Besides the capability of EM in direct detection of virus by visualization, it provides information about the ultrastructure of the virus, its structural dynamics related to the attachment, and replication processes. This makes EM a useful tool in the discovery and design of antiviral agents and vaccines.236−238,242,243 The main techniques of EM microscopy to analyze the virus structures are negative staining, thin sectioning, immune electron microscopy (IEM), cryoelectron microscopy (cryo-EM), tomography, and scanning electron microscopy (SEM). In negative staining, the background is stained, leaving the actual specimen untouched and thus visible. Thin sectioning is used to reduce the viruses into thin layers to be transparent for electrons. The principle of IEM is the formation of immune complexes of the virus with its associated antibody. The cryo-EM is an imaging procedure used to generate high-resolution 3D images of samples, commonly biological materials and cells. In this method, a series of two-dimensional (2D) images of sample are captured while it is tilted at different angles, and then, the 2D images are merged to reconstruct 3D image.243−245 On the other hand, SEM has also been used for virus quantification procedures.246 It should be noted that SEM if combined with the abilities of transmission electron microscopy (TEM) can also provide high-resolution images of virus structure with minute structural details for improved characterization of viruses.237 In the past using EM techniques, SARS was classified as a coronavirus.247,248 EM was used in the recognition of MERS,249 and the image of COVID-19 virus was taken by TEM,250,251 see Figure 11 for more details.
Figure 11.
Visualization of SARS-CoV2 with TEM. The virus is shown in blue color. Reprinted with permission from ref (38). Copyright 2020 American Chemical Society.
Recently cryo-EM was used to describe multiple monoclonal antibodies targeting SARS-CoV2, specific to the S protein identified from memory B cells, from an individual who was infected with SARS-CoV in 2003.42 One antibody, named S309, potently neutralizes SARS-CoV2 and SARS-CoV. Using cryo-EM and binding assays, it was also revealed that S309 recognizes a glycan-containing epitope which is conserved within the sarbecovirus subgenus, without competing with the receptor attachment.250 In ref (252), the cryo-EM is used to determine a 2.9 Å-resolution structure of the RNA-dependent RNA polymerase nsp12 of CoV 19, which catalyzes the synthesis of viral RNA, in complex with two cofactors, nsp7 and nsp8. In ref (253), the 3D structure of the causative agent in SARS infection and the comprehensive information about the morphological surface of SARS-CoV was obtained. This included enhanced visualization of the trimeric structure in the 10–20 nm spikes of the virus surface. These results support the characterization of the SARS agent and development of antiviral strategies,253 and we envisage that such techniques can easily be extended to reveal the details of CoV 19 viruses.
X-ray Crystallography
The XRD from single crystals is a promising method to obtain the highest resolution of the virus structure and visualize the macromolecular assemblies at an atomic level. Generally, there are five steps to identify the virus structure using X-ray crystallography techniques which include: preparation and purification of virus particles, crystallization and mounting the particle crystal, measurement of diffracted data, calculation of phase through molecular replacement (MR) or isomorphous replacement,254,255 and finally, map interpretation and building the model.256−258
To prepare and purify the viruses from the extracellular/tissue culture media, purification kits are readily accessible from the commercial sources.259 During the preparation steps, to maintain the icosahedral symmetry of the virus, the handling procedures should be performed as gentle as possible. Following the purification stage, crystallization is required to bring the aqueous protein solution to supersaturation. There are two phases in crystallization: nucleation and growth. In nucleation, the crystal nucleus is formed at the critical size of molecular aggregation, then a subsequent process of growth is initiated. For growing the crystals of virus samples, there are four methods, batch crystallization, dialysis, liquid–liquid diffusion, and vapor diffusion.260
To improve the resolution and prevent the secondary radiation damage to the virus sample in the collection process of X-ray data, the crystallographic data are collected at cryogenic temperature, typically around 100 K. Growing high-quality virus crystal is one of the challenges in X-ray crystallography of the virus particles. Methods currently in use are time-consuming and based on trial and error procedures. Nevertheless, cryo-EM/ET can be a powerful complementary method for virus protein crystallography.261 Information provided by 3D cryo-EM can be integrated with the collected X-ray data to improve the built model of virus particles.262
Serial femtosecond crystallography (SFX) is another type of X-ray crystallography that uses X-ray free-electron lasers (XFELs). Due to the pulse duration in femtosecond and unparalleled brilliance of XFEL beams, this technique is useful to study the viruses as both a crystal and single particle. Using time-resolved SFX, the changes during life cycles of viruses such as their response to the variation of pH and viral protein interaction with the receptors can be detected.234,263−265 Therefore, the combination of high-resolution static structure of viruses obtained by cryo-EM and dynamic results of conformational changes at room temperature using SFX can be a breakthrough toward producing time-resolved virus structures.266 The use of X-ray crystallography methods to study the family of coronaviruses shows the ability of the tool for in-depth analysis of COVID-19.267−269 Recently, in ref (270), the 3D crystal structure of the SARS-CoV2 unliganded Mpro at 1.75 Å resolution is determined (see Figure 12 for more details), and it was used to guide the optimization of a series of α-ketoamide inhibitors. Such structural analysis forms a strong basis for further development of these compounds to antiviral drugs. However, both common EM and XRD methods are based on the averaging of the numerous particles present in the electron micrographs or crystal. Therefore, there is a limitation in the information obtained from these structural differences that can exist between the individual particles in a large population. Besides, these methods require environments which are far away from the physiological conditions in which the virus normally operates and precludes the characterization of their dynamic properties in real time. This complicates structural studies of viruses that lack a well-defined symmetry such as the SARS-CoV2 as an enveloped virus. A third, direct imaging technology that provides significant impact on virus study is AFM. AFM provides possibilities for studying the COVID-19 virus particles, complementing classical EM and XRD studies.
Figure 12.
The 3D structure of SARS-CoV2 Mpro, in two different views. One protomer of the dimeriz shown in light blue and the other one in orange. Amino acid residues of the catalytic site are indicated as yellow and blue spheres, for Cys145 and His41, respectively. Black spheres indicate the positions of Ala285 of each of the two domains III (see text). Chain termini are labeled N and C for molecule A (lightblue) and N* and C* for molecule B (orange). Reproduced with permission under a Creative Commons CC-BY license from ref (270). Copyright 2020 American Association for the Advancement of Science.
Atomic Force Microscopy
AFM has become a powerful instrument for the visualization and characterization of nanoscale imaging of samples in air and in liquid.271 Typically, in AFM measurement, the deflection of microcantilever is used to probe the interaction between the surface of the sample and the nanometric tip located at the end of microcantilever. Based on the time scales, type, and range of interaction, an extensive range of information including atomistic details of the surface, mechanical, physical, chemical, thermal, and viscoelastic properties of nano/bio materials can be obtained.272−287 The earliest invented mode of AFM was the contact mode, where the tip is raster scanned through the sample surface while maintaining a constant force applied to the sample through deflection detection by adjusting the height of the tip. The main challenges in using contact mode to image soft materials such as viruses and other biological specimens is the applied force to avoid the reversible or irreversible deformation, unwanted friction, and damage to the sample.288 To improve the image resolution and minimize the applied force and friction, the dynamic AFM was invented.289 Generally, in dynamic AFM, the microcantilever is oscillated usually close to its resonance frequency while scanning the sample surface.
Two major dynamic AFM modes are amplitude modulation atomic force microscopy (AM- AFM) (also known as tapping mode) and frequency modulation atomic force microscopy (FM-AFM).289 In the dynamic mode, due to the decrease of the contact time between tip and sample to the small fraction of oscillation period, the friction is minimized and the risk of damage to the sample is significantly reduced. In addition, of these two common modes of dynamic AFM, recently, several advanced techniques are developed to increase the resolution and explore more information from the materials during imaging. Due to the dynamic nature of these advanced techniques, they can be classified as multifrequency methods,281,289 where the cantilever is excited at different frequencies and different signals are used as feedback parameters.25 In addition to the ability to visualize and image virus particles, AFM has the capability to measure and quantify the mechanical and structural properties of virus particles.290,291 Moreover, AFM can be used to manipulate and dissect biological materials including viruses.292−295
To extract and quantify viscoelastic, chemical, and physical properties of viruses, the force–distance curve-based AFM (FD-based AFM) and recently multifrequency approaches are the common methods. In modern FD curve-based AFM, numerous FD curves are recorded during imaging the biological sample.296 Based on the principle of FD-based imaging, the method to map chemical and biological properties is developed.297,298 In this method, the tips are functionalized by specific ligands. Then, based on the adhesion and mechanical strength of bonds formed between functionalized tip and receptors of the samples, the biological properties can be explored during imaging.25,291 The main challenge of FD-based AFM is the high volume and time of data acquisition.25 To increase the speed of imaging and reduce the amount of collected data during measurement, high-speed AFM and multifrequency methods are invented.299,300 Several multifrequency AFM methods have been proposed, however due to their complex physical principle, theoretical development and mechanism of interpretation of data are under more investigation.299,301 The previous studies of coronaviruses using AFM techniques302−305 show the significant potential of AFM to image, visualize, and explore the morphological features of the SARS-CoV virus; see Figure 13 as a typical AFM characterization. Generally, visualization/characterization devices are useful as they provide following information: (1) quantify the surface and subsurface properties of the affected cell by COVID-19 in real time; (2) detect multiple dynamic interactions between SARS-CoV2 virus and host cell; and (3) study the mechanism of SARS-CoV2 virus to cross the cellular membrane and deliver its genome inside the host cells and (4) its replication inside cell and characterization of the nucleic acids of SARS-CoV2 virus, to understand how it is condensed and packaged inside capsids. Recent proposed techniques to nanoscale map directional flow patterns also can be used to study the effects of different pH and ion-specific sieving properties of the host cell on the binding of S protein of SARS-CoV2 virus with receptor at the surface of the cell.306 In fact, very recently Yang et al. characterized molecular binding and inhibition interactions of SARS-CoV2 with ACE2 receptors with AFM.307
Figure 13.
(A) 2D and (B) 3D AFM images and contour map (C) of a single SARS-CoV virion. Scale bar = 100 nm in (A) and (C). The corresponding cursor profiles (middle and bottom row) provide quantitative measurements of the dimensions for the spike proteins (1–15) displayed in (C). Reprinted with permission from ref (302). Copyright 2020 John Wiley and Sons.
Technology and Policies
While academia has made tremendous progress in the field of biosensors, a major challenge remains in overcoming the difficulties associated with translating the laboratory research quickly into commercially viable prototypes by industry and addressing the complex regulatory issues required for clinical settings, especially during pandemics. The good news is that slowly the technology transfer investments from government and industry is on the rise. In addition, regulatory policies from the government in many countries are becoming increasingly business friendly with the academics. This is evident from the fact that a lot of organizations are now identifying and bridging gaps between the academic research and industrial needs via workshops and university-funded proof-of-concept projects. Moreover, specialized urgent funds without limits are set up by many governments during an infectious disease outbreak such as for the COVID-19. On the other hand, instrumentation such as SPR, AFM, XRD, and EM, while well-established and developed from the infrastructure viewpoint, has a high cost which limits its use mostly to laboratory testing.308,309 Still, large-scale industries, especially the ones involved in the vaccine development and testing, can potentially reveal morphological features.310
However, one significant challenge in the technology transfer of most of the biosensors and advanced instrumentation is the capacity building of human resources. The main reason is that practical hands on skills from subjects which are fundamentally far from each other such as biology and electronics are often required with the operator of the sensing instrumentation. This limitation is also being addressed by many educational institutions where courses and projects are encouraged to be interdisciplinary in nature. Other technical challenges for fast technology transfer during pandemics which remain elusive are quick adoption of the biosensor manufacturing protocols and reliability of the sensors. As most of the quickly developed technologies are customized for the pandemic strains, reproducibility, stability, and reliability tests can take more than a few months of time to generate statistical data for making informed decisions. It is here that fundamental science and engineering need to further discover materials which are inherently physically and chemically stable and invent technologies to provide more reliability in the measurements.
In addition, modular sensing systems also provide a flexibility in usage for different analytical biomarkers. For instance, in a single measuring module, electrodes could potentially be used for the detection of multiple biomarkers of a pandemic strain by simply changing the biorecognition layer or the surface chemistry on the electrode. On the other hand, translation of nanoscale analytical technologies to clinical settings depends on the ability to simplify complex analytical devices for non-experts to use and control. Automation and remote control of devices, which is fast increasing with the advent of the internet of healthcare technology, might provide solutions whereby quick troubleshooting or assistance in calibration can be provided to the medical staff (user) remotely and easily.
Ethics and Technology Adoption
While the innovative sensing technologies have immense potential to revolutionize the healthcare system during a crisis such as the COVID-19, there are important ethical issues revolving around such technologies. Arguments concerning data confidentiality, ownership, and privacy are extremely challenging to address in a short duration of time, leading to a decrease in the adoption of the technology by community members.311,312 Especially, the sensors which aim to provide immediate, detailed, and objective feedback of the measurement remotely to the information management systems must abide to the data protection legislatures of the given region. Even though passing the quality controls related to the data protection has nowadays become fast, the acceptability of these technologies in terms of their reliability remains low. Many medical staff still have concerns whether the developed technology has enough understanding of the biology to be properly informed from the measurement. Moreover, with traditional medicine, before a doctor provides a treatment, they must seek a patient’s consent to reasonably inform them about the risks and benefits associated with the intended treatment. In this context, the risks and benefits of the mobile heath applications and self-use biosensors are not explicit as most often users are less willing to read the terms and conditions which usually pop up while using an application. However, toward the laboratory-based testing, biosensors and surface characterization tools eliminate ethical issues such as those associated with animal and human testing.313,314 For example, if a drug is required to be developed in a short duration during a pandemic, large-scale testing can potentially be performed on high-throughput sensing platforms before it is considered viable to test on animals and humans. Large-scale testing could include a variety of characterizations to reveal molecular affinity, specificity, and cross reactivity with potential compounds inside the organism. Therefore, with the increased use of modern characterization tools and bio/chemical sensors, large uncertainties associated with quick drug development and testing can potentially be eliminated.
Conclusions
Biosensors and nanoscale visualization/characterization tools can be considered as innovative and promising tools that can lead to life saving decisions on treatment and a deep understanding of pandemic strains. As we have witnessed extreme challenges associated with the testing and validation of viruses in COVID-19, we need to be prepared in terms of diagnostics to prevent a spread of any new emerging infectious diseases. Recently, Narvaez et al. suggested seven main recommendations for biosensing community, including the funding agencies: (1) investment; (2) collaboration; (3) mass manufacturing; (4) devices should comply with regulation bodies such as FDA; (5) rigorously satisfy features such as disposability and low sample volume tests; (6) integrate with Internet Of Things (IOT), and (7) ease of use for members of the community.315 With the rapid advancement of nanotechnology, we believe that the sensing mechanism will emerge with a multidisciplinary approach, to enable rapid and on-site detection of viruses for point-of-care diagnosis and prevent epidemics at an early stage.
Acknowledgments
N.B. would like to thank support from Royal Society, grant number IEC\R3\193004 during the course of this work. Z.Y. thanks UK NERC fellowship grant (NE/R013349/2) and Royal Academy of Engineering Frontier Follow-up grant (FF\1920\1\36).
Glossary
Vocabulary
- Soft-biosensor
Portable devices which utilize noncontact modes such as direct thermal imaging to detect biomolecules or disease signatures and subsequent analysis to provide the status of the disease using a customized software-based application right at the site of patient
- mHealth
The practice of medicine and public health supported by mobile devices such as smartphones, tablets, personal digital assistants, and the wireless infrastructure; for example, contract tracing applications can be put in mHealth category of devices
- Wastewater-based epidemiology (WBE)
A bio/chemical sensing approach for analysis of pollutants and biomarkers in raw wastewater to obtain both qualitative and quantitative data on the activity of all inhabitants within a given wastewater shed
- Serial femtosecond crystallography (SFX)
A type of X-ray crystallography technique developed using X-ray free-electron lasers (XFELs) which includes multiple progress in sample delivery, data analysis, and collection
- Immune electron microscopy (IEM)
Any method which uses molecules in interaction with antibodies in coincidence with electron microscopy to localize antigens or at the ultrastructural level
- Multifrequency AFM
A collection of methods where the microcantilever is excited with a range of frequencies and corresponding signals at different frequencies are detected to be used as feedback parameters and information channels to explore the materials properties
- FD-based AFM
The force–distance curves obtained from measuring the vertical force that the tip applies to the surface. This technique can be used to extract mechanical, chemical, and physical properties of nanomaterials and is the basis of probing the ligand–receptor bonds in physiological conditions
The authors declare no competing financial interest.
References
- Walls A. C.; Park Y.-J.; Tortorici M. A.; Wall A.; McGuire A. T.; Veesler D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020, 181, 281–292. 10.1016/j.cell.2020.02.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhou P.; Yang X.-L.; Wang X.-G.; Hu B.; Zhang L.; Zhang W.; Si H.-R.; Zhu Y.; Li B.; Huang C.-L.; Chen H.-D.; Chen J.; Luo Y.; Guo H.; Jiang R.-D.; Liu M.-Q.; Chen Y.; Shen X.-R.; Wang X.; Zheng X.-S.; et al. A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin. Nature 2020, 579, 270–273. 10.1038/s41586-020-2012-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saylan Y.; Erdem Ö.; Ünal S.; Denizli A. An Alternative Medical Diagnosis Method: Biosensors for Virus Detection. Biosensors 2019, 9, 65. 10.3390/bios9020065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mao C.; Liu A.; Cao B. Virus-Based Chemical and Biological Sensing. Angew. Chem., Int. Ed. 2009, 48, 6790–6810. 10.1002/anie.200900231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Flampouri E.; Imar S.; OConnell K.; Singh B. Spheroid-3D and Monolayer-2D Intestinal Electrochemical Biosensor for Toxicity/Viability Testing: Applications in Drug Screening, Food Safety, and Environmental Pollutant Analysis. ACS Sens. 2019, 4, 660–669. 10.1021/acssensors.8b01490. [DOI] [PubMed] [Google Scholar]
- Kilic T.; Soler M.; Fahimi-Kashani N.; Altug H.; Carrara S. Mining the Potential of Label-Free Biosensors for In Vitro Antipsychotic Drug Screening. Biosensors 2018, 8, 6. 10.3390/bios8010006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Komatsu N.; Terai K.; Imanishi A.; Kamioka Y.; Sumiyama K.; Jin T.; Okada Y.; Nagai T.; Matsuda M. A Platform of BRET-FRET Hybrid Biosensors for Optogenetics, Chemical Screening, and In Vivo Imaging. Sci. Rep. 2018, 8, 8984. 10.1038/s41598-018-27174-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Justino C. I.; Rocha-Santos T. A.; Duarte A. C. Review of Analytical Figures of Merit of Sensors and Biosensors in Clinical Applications. TrAC, Trends Anal. Chem. 2010, 29, 1172–1183. 10.1016/j.trac.2010.07.008. [DOI] [Google Scholar]
- Melnychuk N.; Egloff S.; Runser A.; Reisch A.; Klymchenko A. S. Review of Analytical Figures of Merit of Sensors and Biosensors in Clinical Applications Light- Harvesting Nanoparticle Probes for FRET-Based Detection of Oligonucleotides with Single-Molecule Sensitivity. Angew. Chem. 2020, 132, 6878–6885. 10.1002/ange.201913804. [DOI] [PubMed] [Google Scholar]
- Kim J.; Campbell A. S.; de Ávila B. E.-F.; Wang J. Wearable Biosensors for Healthcare Monitoring. Nat. Biotechnol. 2019, 37, 389–406. 10.1038/s41587-019-0045-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mimee M.; Nadeau P.; Hayward A.; Carim S.; Flanagan S.; Jerger L.; Collins J.; McDonnell S.; Swartwout R.; Citorik R. J.; Bulović V.; Langer R.; Traverso G.; Chandrakasan A. P.; Lu T. K. An Ingestible Bacterial-Electronic System to Monitor Gastrointestinal Health. Science 2018, 360, 915–918. 10.1126/science.aas9315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alonso-Lomillo M. A.; Domínguez-Renedo O.; Arcos-Martínez M. J. Screen-Printed Biosensors in Microbiology; A Review. Talanta 2010, 82, 1629–1636. 10.1016/j.talanta.2010.08.033. [DOI] [PubMed] [Google Scholar]
- Cetin A. E.; Coskun A. F.; Galarreta B. C.; Huang M.; Herman D.; Ozcan A.; Altug H. Handheld High-Throughput Plasmonic Biosensor Using Computational On- Chip Imaging. Light: Sci. Appl. 2014, 3, e122 10.1038/lsa.2014.3. [DOI] [Google Scholar]
- Ahmed R.; Oldstone M. B.; Palese P. Protective Immunity and Susceptibility to Infectious Diseases: Lessons from the 1918 Influenza Pandemic. Nat. Immunol. 2007, 8, 1188–1193. 10.1038/ni1530. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ramanathan K.; Antognini D.; Combes A.; Paden M.; Zakhary B.; Ogino M.; MacLaren G.; Brodie D.; Shekar K. Planning and Provision of ECMO Services for Severe ARDS during the COVID-19 Pandemic and Other Outbreaks of Emerging Infectious Diseases. Lancet Respir. Med. 2020, 8, 518–526. 10.1016/S2213-2600(20)30121-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kumar D.; Manuel O.; Natori Y.; Egawa H.; Grossi P.; Han S.-H.; Fernandez- Ruiz M.; Humar A. COVID-19: A Global Transplant Perspective on Successfully Navigating a Pandemic. Am. J. Transplant. 2020, 00, 1–7. 10.1111/ajt.15876. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Narayanan K.; Makino S. Cooperation of an RNA Packaging Signal and a Viral Envelope Protein in Coronavirus RNA Packaging. J. Virol. 2001, 75, 9059–9067. 10.1128/JVI.75.19.9059-9067.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cella L. N.; Blackstock D.; Yates M. A.; Mulchandani A.; Chen W. Detection of RNA Viruses: Current Technologies and Future Perspectives. Crit. Rev. Eukaryotic Gene Expression 2013, 23, 125–137. 10.1615/CritRevEukaryotGeneExpr.2013006974. [DOI] [PubMed] [Google Scholar]
- Caygill R. L.; Blair G. E.; Millner P. A. A Review on Viral Biosensors to Detect Human Pathogens. Anal. Chim. Acta 2010, 681, 8–15. 10.1016/j.aca.2010.09.038. [DOI] [PubMed] [Google Scholar]
- Roether J.; Chu K.-Y.; Willenbacher N.; Shen A. Q.; Bhalla N. Real-Time Monitoring of DNA Immobilization and Detection of DNA Polymerase Activity by a Microfluidic Nanoplasmonic Platform. Biosens. Bioelectron. 2019, 142, 111528. 10.1016/j.bios.2019.111528. [DOI] [PubMed] [Google Scholar]
- Bianchi N.; Rutigliano C.; Tomassetti M.; Feriotto G.; Zorzato F.; Gambari R. Biosensor Technology and Surface Plasmon Resonance for Real-Time Detection of HIV1 Genomic Sequences Amplified by Polymerase Chain Reaction. J. Clin. Virol. 1997, 8, 199–208. 10.1016/S0928-0197(97)00025-1. [DOI] [PubMed] [Google Scholar]
- Tang L.; Zeng G.; Shen G.; Li Y.; Liu C.; Li Z.; Luo J.; Fan C.; Yang C. Sensitive Detection of Lip Genes by Electrochemical DNA Sensor and its Application in Polymerase Chain Reaction Amplicons from Phanerochaete Chrysosporium. Biosens. Bioelectron. 2009, 24, 1474–1479. 10.1016/j.bios.2008.09.031. [DOI] [PubMed] [Google Scholar]
- Jeong M.-S.; Ahn D.-R. A Microwell Plate-Based Multiplex Immunoassay for Simultaneous Quantitation of Antibodies to Infectious Viruses. Analyst 2015, 140, 1995–2000. 10.1039/C4AN02262A. [DOI] [PubMed] [Google Scholar]
- Kuznetsov Y. G.; Daijogo S.; Zhou J.; Semler B. L.; McPherson A. Atomic Force Microscopy Analysis of Icosahedral Virus RNA. J. Mol. Biol. 2005, 347, 41–52. 10.1016/j.jmb.2005.01.006. [DOI] [PubMed] [Google Scholar]
- Dufrêne Y. F.; Martínez-Martín D.; Medalsy I.; Alsteens D.; Müller D. J. Multiparametric Imaging of Biological Systems by Force-Distance Curve-Based AFM. Nat. Methods 2013, 10, 847–854. 10.1038/nmeth.2602. [DOI] [PubMed] [Google Scholar]
- Lin Y.; Yan X.; Cao W.; Wang C.; Feng J.; Duan J.; Xie S. Short Communication Probing the Structure of the SARS Coronavirus Using Scanning Electron Microscopy. Antiviral Ther. 2004, 9, 287–289. [PubMed] [Google Scholar]
- Hsu C.-H.; Ko T.-P.; Yu H.-M.; Tang T.-K.; Chen S.-T.; Wang A. H.-J. Immunological, Structural, and Preliminary X-Ray Diffraction Characterizations of the Fusion Core of the SARS-Coronavirus Spike Protein. Biochem. Biophys. Res. Commun. 2004, 324, 761–767. 10.1016/j.bbrc.2004.09.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Papageorgiou N.; Lichiere J.; Baklouti A.; Ferron F.; Sévajol M.; Canard B.; Coutard B. Structural Characterization of the N-Terminal Part of the MERS- CoV Nucleocapsid by X-Ray Diffraction and Small-Angle X-Ray Scattering. Acta Crystallogr. D Struct Biol. 2016, 72, 192–202. 10.1107/S2059798315024328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cascella M.; Rajnik M.; Cuomo A.; Dulebohn S. C.; Di Napoli R.. Features, Evaluation and Treatment Coronavirus (COVID-19). In StatPearls; StatPearls Publishing, Treasure Island, FL, 2020. [PubMed] [Google Scholar]
- Simmonds P.; Davidson F.; Lycett C.; Prescott L. E.; MacDonald D. M.; Ellender J.; Yap P. L.; Haydon G. H.; Gillon J.; Jarvis L. M.; et al. Detection of a Novel DNA Virus (TT Virus) in Blood Donors and Blood Products. Lancet 1998, 352, 191–195. 10.1016/S0140-6736(98)03056-6. [DOI] [PubMed] [Google Scholar]
- Fehr A. R.; Perlman S. Coronaviruses: An Overview of their Replication and Pathogenesis. Methods Mol. Biol. 2015, 1282, 1–23. 10.1007/978-1-4939-2438-7_1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Korsman S. N.; et al. Human Coronaviruses. Virology 2012, 94–95. 10.1016/B978-0-443-07367-0.00040-9. [DOI] [Google Scholar]
- Neuman B. W.; Adair B. D.; Yoshioka C.; Quispe J. D.; Orca G.; Kuhn P.; Milligan R. A.; Yeager M.; Buchmeier M. J. Supramolecular Architecture of Severe Acute Respiratory Syndrome Coronavirus Revealed by Electron Cryomicroscopy. J. Virol. 2006, 80, 7918–7928. 10.1128/JVI.00645-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mousavizadeh L.; Ghasemi S.. Genotype and Phenotype of COVID-19: Their Roles in Pathogenesis. J. Microbiol. Immunol. Infect. 2020, 10.1016/j.jmii.2020.03.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ai T.; Yang Z.; Hou H.; Zhan C.; Chen C.; Lv W.; Tao Q.; Sun Z.; Xia L. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 2020, 200642. 10.1148/radiol.2020200642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spackman E.; Senne D. A.; Myers T. J.; Bulaga L. L.; Garber L. P.; Perdue M. L.; Lohman K.; Daum L. T.; Suarez D. L. Development of a Real-Time Reverse Transcriptase PCR Assay for Type A Influenza Virus and the Avian H5 and H7 Hemagglutinin Subtypes. J. Clin. Microbiol. 2002, 40, 3256–3260. 10.1128/JCM.40.9.3256-3260.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wu J.; Liu J.; Li S.; Peng Z.; Xiao Z.; Wang X.; Yan R.; Luo J. Detection and Analysis of Nucleic Acid in Various Biological Samples of COVID-19 Patients. Travel Med. Infect. Dis. 2020, 101673. 10.1016/j.tmaid.2020.101673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Udugama B.; Kadhiresan P.; Kozlowski H. N.; Malekjahani A.; Osborne M.; Li V. Y.; Chen H.; Mubareka S.; Gubbay J.; Chan W. C. Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano 2020, 14, 3822–3835. 10.1021/acsnano.0c02624. [DOI] [PubMed] [Google Scholar]
- Marras S. A. Selection of Fluorophore and Quencher Pairs for Fluorescent Nucleic Acid Hybridization Probes. Methods Mol. Biol. 2006, 335, 3–16. 10.1385/1-59745-069-3:3. [DOI] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention . https://www.cdc.gov/coronavirus/2019-ncov/lab/rt-pcr-panel-primer-probes.html (accessed on 2020-05-22).
- Li D.; Wang D.; Dong J.; Wang N.; Huang H.; Xu H.; Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J. Radiol. 2020, 21, 505–508. 10.3348/kjr.2020.0146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xiao A. T.; Tong Y. X.; Zhang S.. False-Negative of RT-PCR and Prolonged Nucleic Acid Conversion in COVID-19: Rather Than Recurrence. J. Med. Virol. 2020, 10.1002/jmv.25855. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang W.; Yan F. Patients With RT-PCR Confirmed COVID-19 and Normal Chest CT. Radiology 2020, 295, 200702. 10.1148/radiol.2020200702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chua F.; Armstrong-James D.; Desai S. R.; Barnett J.; Kouranos V.; Kon O. M.; José R.; Vancheeswaran R.; Loebinger M. R.; Wong J.; et al. The Role of CT in Case Ascertainment and Management of COVID-19 Pneumonia in the UK: Insights from High-Incidence Regions. Lancet Respir. Med. 2020, 8, 438–440. 10.1016/S2213-2600(20)30132-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shi H.; Han X.; Jiang N.; Cao Y.; Alwalid O.; Gu J.; Fan Y.; Zheng C. Radiological Findings from 81 Patients with COVID-19 Pneumonia in Wuhan, China: A Descriptive Study. Lancet Infect. Dis. 2020, 20, 425–434. 10.1016/S1473-3099(20)30086-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ai T.; Yang Z.; Hou H.; Zhan C.; Chen C.; Lv W.; Tao Q.; Sun Z.; Xia L. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19)- in China: A Report of 1014 Cases. Radiology 2020, 200642. 10.1148/radiol.2020200642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- To K. K.-W.; Tsang O. T.-Y.; Leung W.-S.; Tam A. R.; Wu T.-C.; Lung D. C.; Yip C. C.-Y.; Cai J.-P.; Chan J. M.-C.; Chik T. S.-H.; et al. Temporal Profiles of Viral Load in Posterior Oropharyngeal Saliva Samples and Serum Antibody Responses During Infection by SARS-CoV-2: An Observational Cohort Study. Lancet Infect. Dis. 2020, 20, 565–574. 10.1016/S1473-3099(20)30196-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lv H.; Wu N. C.; Tsang O. T.-Y.; Yuan M.; Perera R. A.; Leung W. S.; So R. T.; Chan J. M. C.; Yip G. K.; Chik T. S. H.; et al. Cross-Reactive Antibody Response Between SARS-CoV-2 and SARS-CoV Infections. Cell Rep. 2020, 31, 107725. 10.1016/j.celrep.2020.107725. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tang Y.-W.; Schmitz J. E.; Persing D. H.; Stratton C. W. The Laboratory Diagnosis of COVID-19 Infection: Current Issues and Challenges. J. Clin. Microbiol. 2020, 58, e00512–e00520. 10.1128/JCM.00512-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Petherick A. Developing Antibody Tests for SARS-CoV-2. Lancet 2020, 395, 1101–1102. 10.1016/S0140-6736(20)30788-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hellewell J.; Abbott S.; Gimma A.; Bosse N. I.; Jarvis C. I.; Russell T. W.; Munday J. D.; Kucharski A. J.; Edmunds W. J.; et al. Feasibility of Controlling COVID-19 Outbreaks by Isolation of Cases and Contacts. Lancet Glob. Health 2020, 8, 488–496. 10.1016/S2214-109X(20)30074-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ferretti L.; Wymant C.; Kendall M.; Zhao L.; Nurtay A.; Abeler-Dörner L.; Parker M.; Bonsall D.; Fraser C. Quantifying SARS-CoV-2 Transmission Suggests Epidemic Control with Digital Contact Tracing. Science 2020, 368, eabb6936 10.1126/science.abb6936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cho H.; Ippolito D.; Yu Y. W.. Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-Offs. arXiv (Cryptography and Security), March 30, 2020, 2003.11511, ver. 2. https://arxiv.org/abs/2003.11511 (accessed on May 25, 2020).
- Wang C.; Zheng X.; Gai W.; Zhao Y.; Wang H.; Wang H.; Feng N.; Chi H.; Qiu B.; Li N.; Wang T.; Gao Y.; Yang S.; Xia X. MERS-CoV Virus-Like Particles Produced in Insect Cells Induce Specific Humoural and Cellular Imminity in Rhesus Macaques. Oncotarget 2017, 8, 12686–12694. 10.18632/oncotarget.8475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hulswit R. J.; de Haan C. A.; Bosch B. J. Coronavirus Spike Protein and Tropism Changes. Adv. Virus Res. 2016, 96, 29–57. 10.1016/bs.aivir.2016.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schoeman D.; Fielding B. C. Coronavirus Envelope Protein: Current Knowledge. Virol. J. 2019, 16, 1–22. 10.1186/s12985-019-1182-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Siu Y. L.; Teoh K. T.; Lo J.; Chan C. M.; Kien F.; Escriou N.; Tsao S. W.; Nicholls J. M.; Altmeyer R.; Peiris J. S. M.; Bruzzone R.; Nal B. The M, E, and N Structural Proteins of the Severe Acute Respiratory Syndrome Coronavirus Are Required for Efficient Assembly, Trafficking, and Release of Virus-Like Particles. J. Virol. 2008, 82, 11318–11330. 10.1128/JVI.01052-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McBride R.; van Zyl M.; Fielding B. C. The Coronavirus Nucleocapsid Is a Multifunctional Protein. Viruses 2014, 6, 2991–3018. 10.3390/v6082991. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Neuman B. W.; Kiss G.; Kunding A. H.; Bhella D.; Baksh M. F.; Connelly S.; Droese B.; Klaus J. P.; Makino S.; Sawicki S. G.; Siddell S. G.; Stamou D. G.; Wilson I. A.; Kuhn P.; Buchmeier M. J. A Structural Analysis of M Protein in Coronavirus Assembly and Morphology. J. Struct. Biol. 2011, 174, 11–22. 10.1016/j.jsb.2010.11.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Masters P. S. The Molecular Biology of Coronaviruses. Adv. Virus Res. 2006, 66, 193–292. 10.1016/S0065-3527(06)66005-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kirchdoerfer R. N.; Cottrell C. A.; Wang N.; Pallesen J.; Yassine H. M.; Turner H. L.; Corbett K. S.; Graham B. S.; McLellan J. S.; Ward A. B. Pre-Fusion Structure of a Human Coronavirus Spike Protein. Nature 2016, 531, 118–121. 10.1038/nature17200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gheblawi M.; Wang K.; Viveiros A.; Nguyen Q.; Zhong J.-C.; Turner A. J.; Raizada M. K.; Grant M. B.; Oudit G. Y. Angiotensin Converting Enzyme 2: SARS- CoV-2 Receptor and Regulator of the Renin-Angiotensin System. Circ. Res. 2020, 126, 1456–1474. 10.1161/CIRCRESAHA.120.317015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Walls A. C.; Park Y. J.; Tortorici M. A.; Wall A.; McGuire A. T.; Veesler D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell 2020, 181, 281–292. 10.1016/j.cell.2020.02.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xia S.; Zhu Y.; Liu M.; Lan Q.; Xu W.; Wu Y.; Ying T.; Liu S.; Shi Z.; Jiang S.; Lu L. Fusion Mechanism of 2019-nCoV and Fusion Inhibitors Targeting HR1 Domain in Spike Protein. Cell. Mol. Immunol. 2020, 3–5. 10.1038/s41423-020-0374-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Han Q.; Lin Q.; Jin S.; You L. Coronavirus 2019-nCoV: A Brief Perspective from the Front Line Qingmei. J. Infect. 2020, 80, 373–377. 10.1016/j.jinf.2020.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xu Z.; Shi L.; Wang Y.; Zhang J.; Huang L.; Zhang C.; Liu S.; Zhao P.; Liu H.; Zhu L.; et al. Pathological Findings of COVID-19 Associated with Acute Respiratory Distress Syndrome. Lancet Respir. Med. 2020, 8, 420–422. 10.1016/S2213-2600(20)30076-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gong J.; Dong H.; Xia S. Q.; Huang Y. Z.; Wang D.; Zhao Y.; Liu W.; Tu S.; Zhang M.; Wang Q.; Lu F.. Correlation Analysis between Disease Severity and Inflammation-Related Parameters in Patients with COVID-19 Pneumonia. MedRxiv, February 27, 2020. 10.1101/2020.02.25.20025643 (accessed on 2020-05-22). [DOI] [PMC free article] [PubMed]
- XIANG J.; Wen J.; Yuan X.; Xiong S.; Zhou X.; Liu C.; Min X.. Potential Biochemical Markers to Identify Severe Cases Among COVID-19 Patients. medRxiv. March 23, 2020. 10.1101/2020.03.19.20034447 (accessed on 2020-05-18). [DOI]
- Moein S. T.; Hashemian S. M.; Mansourafshar B.; Khorram-Tousi A.; Tabarsi P.; Doty R. L. Smell Dysfunction: A Biomarker for COVID-19. Int. Forum Allergy Rh. 2020, 22587. 10.1002/alr.22587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bowman G. L. Biomarkers for Early Detection of Parkinson Disease: A Scent of Consistency with Olfactory Dysfunction. Neurology 2017, 89, 1432–1434. 10.1212/WNL.0000000000004383. [DOI] [PubMed] [Google Scholar]
- Galani I. E.; Andreakos E. Neutrophils in Viral Infections: Current Concepts and Caveats. J. Leukocyte Biol. 2015, 98, 557–564. 10.1189/jlb.4VMR1114-555R. [DOI] [PubMed] [Google Scholar]
- Miedzybrodzki R.; Switala-Jelen K.; Fortuna W.; Weber-Dabrowska B.; Przerwa A.; Lusiak-Szelachowska M.; Dabrowska K.; Kurzepa A.; Boratynski J.; Syper D.; Pozniak G.; Lugowski C.; Gorski A. Bacteriophage Preparation Inhibition of Reactive Oxygen Species Generation by Endotoxin-Stimulated Polymorphonuclear Leukocytes. Virus Res. 2008, 131, 233–242. 10.1016/j.virusres.2007.09.013. [DOI] [PubMed] [Google Scholar]
- Goody M. F.; Peterman E.; Sullivan C.; Kim C. H. Quantification of the Respiratory Burst Response as an Indicator of Innate Immune Health in Zebrafish. J. Visualized Exp. 2013, 79, e50667 10.3791/50667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Erard M.; Dupré-Crochet S.; Nüße O. Biosensors for Spatiotemporal Detection of Reactive Oxygen Species in Cells and Tissues. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2018, 314, 667–683. 10.1152/ajpregu.00140.2017. [DOI] [PubMed] [Google Scholar]
- Henry B. M.; de Oliveira M. H. S.; Benoit S.; Plebani M.; Lippi G. Hematologic, Biochemical and Immune Biomarker Abnormalities Associated with Severe Illness and Mortality in Coronavirus Disease 2019 (COVID-19): A Meta-Analysis. Clin. Chem. Lab. Med. 2020, 58, 1021–1028. 10.1515/cclm-2020-0369. [DOI] [PubMed] [Google Scholar]
- Weis W.; Brown J. H.; Cusack S.; Paulson J. C.; Skehel J. J.; Wiley D. C. Structure of the Influenza Virus Haemagglutinin Complexed with Its Receptor. Nature 1988, 333, 426–431. 10.1038/333426a0. [DOI] [PubMed] [Google Scholar]
- Green N.; Alexander H.; Olson A.; Alexander S.; Shinnick T. M.; Sutcliffe J. G.; Lerner R. A. Immunogenic Structure of the Influenza Virus Hemagglutinin. Cell 1982, 28, 477–487. 10.1016/0092-8674(82)90202-1. [DOI] [PubMed] [Google Scholar]
- Nidzworski D.; Pranszke P.; Grudniewska M.; Król E.; Gromadzka B. Universal Biosensor for Detection of Influenza Virus. Biosens. Bioelectron. 2014, 59, 239–242. 10.1016/j.bios.2014.03.050. [DOI] [PubMed] [Google Scholar]
- Rowe C. A.; Tender L. M.; Feldstein M. J.; Golden J. P.; Scruggs S. B.; MacCraith B. D.; Cras J. J.; Ligler F. S. Array Biosensor for Simultaneous Identification of Bacterial, Viral, and Protein Analytes. Anal. Chem. 1999, 71, 3846–3852. 10.1021/ac981425v. [DOI] [PubMed] [Google Scholar]
- Rossi A. M.; Wang L.; Reipa V.; Murphy T. E. Porous Silicon Biosensor for Detection of Viruses. Biosens. Bioelectron. 2007, 23, 741–745. 10.1016/j.bios.2007.06.004. [DOI] [PubMed] [Google Scholar]
- Formisano N.; Jolly P.; Bhalla N.; Cromhout M.; Flanagan S. P.; Fogel R.; Limson J. L.; Estrela P. Optimisation of an Electrochemical Impedance Spectroscopy Aptasensor by Exploiting Quartz Crystal Microbalance with Dissipation Signals. Sens. Actuators, B 2015, 220, 369–375. 10.1016/j.snb.2015.05.049. [DOI] [Google Scholar]
- Miodek A.; Regan E. M.; Bhalla N.; Hopkins N. A.; Goodchild S. A.; Estrela P. Optimisation and Characterisation of Anti-Fouling Ternary SAM Layers for Impedance- Based Aptasensors. Sensors 2015, 15, 25015–25032. 10.3390/s151025015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xi X.; Wu D.; Ji W.; Zhang S.; Tang W.; Su Y.; Guo X.; Liu R. Manipulating the Sensitivity and Selectivity of OECT-Based Biosensors via the Surface Engineering of Carbon Cloth Gate Electrodes. Adv. Funct. Mater. 2020, 30, 1905361. 10.1002/adfm.201905361. [DOI] [Google Scholar]
- Ge X.; Asiri A. M.; Du D.; Wen W.; Wang S.; Lin Y. Nanomaterial-Enhanced Paper-Based Biosensors. TrAC, Trends Anal. Chem. 2014, 58, 31–39. 10.1016/j.trac.2014.03.008. [DOI] [Google Scholar]
- del Río J. S.; Henry O. Y.; Jolly P.; Ingber D. E. An Antifouling Coating That Enables Affinity-Based Electrochemical Biosensing in Complex Biological Fluids. Nat. Nanotechnol. 2019, 14, 1143–1149. 10.1038/s41565-019-0566-z. [DOI] [PubMed] [Google Scholar]
- Chaki N. K.; Vijayamohanan K. Self-Assembled Monolayers as a Tunable Platform for Biosensor Applications. Biosens. Bioelectron. 2002, 17, 1–12. 10.1016/S0956-5663(01)00277-9. [DOI] [PubMed] [Google Scholar]
- Zhou N.; Wang J.; Chen T.; Yu Z.; Li G. Enlargement of Gold Nanoparticles on the Surface of a Self-Assembled Monolayer Modified Electrode: A Mode in Biosensor Design. Anal. Chem. 2006, 78, 5227–5230. 10.1021/ac0605492. [DOI] [PubMed] [Google Scholar]
- Sethi R. S. Transducer Aspects of Biosensors. Biosens. Bioelectron. 1994, 9, 243–264. 10.1016/0956-5663(94)80127-4. [DOI] [Google Scholar]
- Gao A.; Zou N.; Dai P.; Lu N.; Li T.; Wang Y.; Zhao J.; Mao H. Signal-To-Noise Ratio Enhancement of Silicon Nanowires Biosensor with Rolling Circle Amplification. Nano Lett. 2013, 13, 4123–4130. 10.1021/nl401628y. [DOI] [PubMed] [Google Scholar]
- Cannon B.; Campos A. R.; Lewitz Z.; Willets K. A.; Russell R. Zeptomole Detection of DNA Nanoparticles by Single-Molecule Fluorescence with Magnetic Field-Directed Localization. Anal. Biochem. 2012, 431, 40–47. 10.1016/j.ab.2012.08.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wu K.; Klein T.; Krishna V. D.; Su D.; Perez A. M.; Wang J.-P. Portable Gmr Handheld Platform for the Detection of Influenza a Virus. ACS Sens. 2017, 2, 1594–1601. 10.1021/acssensors.7b00432. [DOI] [PubMed] [Google Scholar]
- Rasheed P. A.; Sandhyarani N. A Highly Sensitive DNA Sensor for Attomolar Detection of the BRCA1 Gene: Signal Amplification with Gold Nanoparticle Clusters. Analyst 2015, 140, 2713–2718. 10.1039/C5AN00004A. [DOI] [PubMed] [Google Scholar]
- Nam J.-M.; Thaxton C. S.; Mirkin C. A. Nanoparticle-Based Bio-Bar Codes for the Ultrasensitive Detection of Proteins. Science 2003, 301, 1884–1886. 10.1126/science.1088755. [DOI] [PubMed] [Google Scholar]
- Puleo C. M.; Wang T.-H. Microfluidic Means of Achieving Attomolar Detection Limits with Molecular Beacon Probes. Lab Chip 2009, 9, 1065–1072. 10.1039/b819605b. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tekin H. C.; Cornaglia M.; Gijs M. A. Attomolar Protein Detection Using a Magnetic Bead Surface Coverage Assay. Lab Chip 2013, 13, 1053–1059. 10.1039/c3lc41285g. [DOI] [PubMed] [Google Scholar]
- Yoo Y. K.; Kim G.; Park D.; Kim J.; Kim Y.; Kim H. Y.; Yang S. H.; Lee J. H.; Hwang K. S. Gold Nanoparticles Assisted Sensitivity Improvement of Interdigitated Microelectrodes Biosensor for Amyloid-β Detection in Plasma Sample. Sens. Actuators, B 2020, 308, 127710. 10.1016/j.snb.2020.127710. [DOI] [Google Scholar]
- Xu S.; Zhan J.; Man B.; Jiang S.; Yue W.; Gao S.; Guo C.; Liu H.; Li Z.; Wang J.; Zhou Y. Real-Time Reliable Determination of Binding Kinetics of DNA Hybridization Using a Multi-Channel Graphene Biosensor. Nat. Commun. 2017, 8, 14902. 10.1038/ncomms14902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Buerk D. G.Biosensors: Theory and Applications, 1st ed.; CRC Press: Boca Raton, FL, 1995; pp 35–40. [Google Scholar]
- Hall D. A.; Gaster R. S.; Osterfeld S. J.; Murmann B.; Wang S. X. Gmr Biosensor Arrays: Correction Techniques for Reproducibility and Enhanced Sensitivity. Biosens. Bioelectron. 2010, 25, 2177–2181. 10.1016/j.bios.2010.01.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hall D. A.; Gaster R. S.; Makinwa K. A.; Wang S. X.; Murmann B. A 256 Pixel Magnetoresistive Biosensor Microarray in 0.18 μm CMOS. IEEE J. Solid-State Circuits 2013, 48, 1290–1301. 10.1109/JSSC.2013.2245058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Geissler D.; Charbonnière L. J.; Ziessel R. F.; Butlin N. G.; Löhmannsröben H.-G.; Hildebrandt N. Quantum Dot Biosensors for Ultrasensitive Multiplexed Diagnostics. Angew. Chem., Int. Ed. 2010, 49, 1396–1401. 10.1002/anie.200906399. [DOI] [PubMed] [Google Scholar]
- Li L.; Pan L.; Ma Z.; Yan K.; Cheng W.; Shi Y.; Yu G. All Inkjet-Printed Amperometric Multiplexed Biosensors Based on Nanostructured Conductive Hydrogel Electrodes. Nano Lett. 2018, 18, 3322–3327. 10.1021/acs.nanolett.8b00003. [DOI] [PubMed] [Google Scholar]
- Danilov A.; Tselikov G.; Wu F.; Kravets V. G.; Ozerov I.; Bedu F.; Grigorenko A. N.; Kabashin A. V. Ultra-Narrow Surface Lattice Resonances in Plasmonic Metamaterial Arrays for Biosensing Applications. Biosens. Bioelectron. 2018, 104, 102–112. 10.1016/j.bios.2017.12.001. [DOI] [PubMed] [Google Scholar]
- Varshney M.; Li Y. Interdigitated Array Microelectrodes Based Impedance Biosensors for Detection of Bacterial Cells. Biosens. Bioelectron. 2009, 24, 2951–2960. 10.1016/j.bios.2008.10.001. [DOI] [PubMed] [Google Scholar]
- Mehta S.; Zhang Y.; Roth R. H.; fan Zhang J.; Mo A.; Tenner B.; Huganir R. L.; Zhang J. Single-Fluorophore Biosensors for Sensitive and Multiplexed Detection of Signalling Activities. Nat. Cell Biol. 2018, 20, 1215–1225. 10.1038/s41556-018-0200-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tort N.; Salvador J.-P.; Marco M.-P. Multimodal Plasmonic Biosensing Nanostructures Prepared by DNA-Directed Immobilization of Multifunctional DNA- Gold Nanoparticles. Biosens. Bioelectron. 2017, 90, 13–22. 10.1016/j.bios.2016.11.022. [DOI] [PubMed] [Google Scholar]
- Formisano N.; Bhalla N.; Wong L. C.; Di Lorenzo M.; Pula G.; Estrela P. Multimodal Electrochemical and Nanoplasmonic Biosensors Using Ferrocene- Crowned Nanoparticles for Kinase Drug Discovery Applications. Electrochem. Commun. 2015, 57, 70–73. 10.1016/j.elecom.2015.05.014. [DOI] [Google Scholar]
- Bhalla N.; Estrela P. Exploiting the Signatures of Nanoplasmon-Exciton Coupling on Proton Sensitive Insulator-Semiconductor Devices for Drug Discovery Applications. Nanoscale 2018, 10, 13320–13328. 10.1039/C8NR04540B. [DOI] [PubMed] [Google Scholar]
- Wang B.; Barahona M.; Buck M. A Modular Cell-Based Biosensor Using Engineered Genetic Logic Circuits to Detect and Integrate Multiple Environmental Signals. Biosens. Bioelectron. 2013, 40, 368–376. 10.1016/j.bios.2012.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ramfos I.; Blionas S.; Birbas A. Architecture of a Modular, Multichannel Readout System for Dense Electrochemical Biosensor Microarrays. Meas. Sci. Technol. 2015, 26, 015701 10.1088/0957-0233/26/1/015701. [DOI] [Google Scholar]
- Rose H. M.; Witte C.; Rossella F.; Klippel S.; Freund C.; Schröder L. Development of an Antibody-Based, Modular Biosensor for 129Xe NMR Molecular Imaging of Cells at Nanomolar Concentrations. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 11697–11702. 10.1073/pnas.1406797111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zuo P.; Li X.; Dominguez D. C.; Ye B.-C. A PDMS/Paper/Glass Hybrid Microfluidic Biochip Integrated with Aptamer-Functionalized Graphene Oxide Nano-Biosensors for One-Step Multiplexed Pathogen Detection. Lab Chip 2013, 13, 3921–3928. 10.1039/c3lc50654a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Desmet C.; Marquette C. A.; Blum L. J.; Doumeche B. Paper Electrodes for Bioelectrochemistry: Biosensors and Biofuel Cells. Biosens. Bioelectron. 2016, 76, 145–163. 10.1016/j.bios.2015.06.052. [DOI] [PubMed] [Google Scholar]
- Ge X.; Asiri A. M.; Du D.; Wen W.; Wang S.; Lin Y. Nanomaterial-Enhanced Paper-Based Biosensors. TrAC, Trends Anal. Chem. 2014, 58, 31–39. 10.1016/j.trac.2014.03.008. [DOI] [Google Scholar]
- Farsinezhad S.; Mohammadpour A.; Dalrymple A. N.; Geisinger J.; Kar P.; Brett M. J.; Shankar K. Transparent Anodic TiO2 Nanotube Arrays on Plastic Substrates for Disposable Biosensors and Flexible Electronics. J. Nanosci. Nanotechnol. 2013, 13, 2885–2891. 10.1166/jnn.2013.7409. [DOI] [PubMed] [Google Scholar]
- Kröger S.; Turner A. P. Solvent-Resistant Carbon Electrodes Screen Printed onto Plastic for Use in Biosensors. Anal. Chim. Acta 1997, 347, 9–18. 10.1016/S0003-2670(96)00634-4. [DOI] [Google Scholar]
- Solanki P. R.; Kaushik A.; Agrawal V. V.; Malhotra B. D. Nanostructured Metal Oxide-Based Biosensors. NPG Asia Mater. 2011, 3, 17–24. 10.1038/asiamat.2010.137. [DOI] [Google Scholar]
- Xiao F.; Li Y.; Zan X.; Liao K.; Xu R.; Duan H. Growth of Metal-Metal Oxide Nanostructures on Freestanding Graphene Paper for Flexible Biosensors. Adv. Funct. Mater. 2012, 22, 2487–2494. 10.1002/adfm.201200191. [DOI] [Google Scholar]
- Ferrag C.; Noroozifar M.; Kerman K. Thiol Functionalized Carbon Ceramic Electrode Modified with Multi-Walled Carbon Nanotubes and Gold Nanoparticles for Simultaneous Determination of Purine Derivatives. Mater. Sci. Eng., C 2020, 110, 110568. 10.1016/j.msec.2019.110568. [DOI] [PubMed] [Google Scholar]
- Sun A. C.; Yao C.; Venkatesh A. G.; Hall D. A. An Efficient Power Harvesting Mobile Phone-Based Electrochemical Biosensor for Point-Of-Care Health Monitoring. Sens. Actuators, B 2016, 235, 126–135. 10.1016/j.snb.2016.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gibson T. D.; Hulbert J. N.; Parker S. M.; Woodward J. R.; Higgins I. J. Extended Shelf Life of Enzyme-Based Biosensors Using a Novel Stabilization System. Biosens. Bioelectron. 1992, 7, 701–708. 10.1016/0956-5663(92)85052-C. [DOI] [Google Scholar]
- Hannah S.; Al-Hatmi M.; Gray L.; Corrigan D. K. Low-Cost, Thin-Film, Mass- Manufacturable Carbon Electrodes for Detection of the Neurotransmitter Dopamine. Bioelectrochemistry 2020, 133, 107480. 10.1016/j.bioelechem.2020.107480. [DOI] [PubMed] [Google Scholar]
- Han Y. D.; Chun H. J.; Yoon H. C. Low-Cost Point-Of-Care Biosensors Using Common Electronic Components as Transducers. BioChip J. 2020, 14, 32–47. 10.1007/s13206-020-4104-8. [DOI] [Google Scholar]
- Mohanraj J.; Durgalakshmi D.; Rakkesh A. Current Trends in Disposable Graphene- Based Printed Electrode for Electrochemical Biosensors. J. Electrochem. Soc. 2020, 167, 067523 10.1149/1945-7111/ab818b. [DOI] [Google Scholar]
- Yoo E.-H.; Lee S.-Y. Glucose Biosensors: An Overview of Use in Clinical Practice. Sensors 2010, 10, 4558–4576. 10.3390/s100504558. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang X.; Xu D.; Chen J.; Liu J.; Li Y.; Song J.; Ma X.; Guo J. Smartphone-Based Analytical Biosensors. Analyst 2018, 143, 5339–5351. 10.1039/C8AN01269E. [DOI] [PubMed] [Google Scholar]
- Broeders J.; Croux D.; Peeters M.; Beyens T.; Duchateau S.; Cleij T. J.; Wagner P.; Thoelen R.; De Ceuninck W. Mobile Application for Impedance-Based Biomimetic Sensor Readout. IEEE Sens. J. 2013, 13, 2659–2665. 10.1109/JSEN.2013.2256346. [DOI] [Google Scholar]
- Brindha J.; Chanda K.; Balamurali M. M. Biosensors for Pathogen Surveillance. Environ. Chem. Lett. 2018, 16, 1325–1337. 10.1007/s10311-018-0759-y. [DOI] [Google Scholar]
- Dyer O. Covid-19: US Testing Ramps Up as Early Response Draws Harsh Criticism. BMJ. 2020, 368, m1167. 10.1136/bmj.m1167. [DOI] [PubMed] [Google Scholar]
- Gaur S.; Dumyati G.; Nace D. A.; Jump R. L. Unprecedented Solutions for Extraordinary Times: Helping Long-Term Care Settings Deal with the COVID-19 Pandemic. Infect. Cont. Hosp. Ep. 2020, 41, 729–730. 10.1017/ice.2020.98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Palenzuela C. L. M.; Pumera M. (Bio) Analytical Chemistry Enabled by 3D Printing: Sensors and Biosensors. TrAC, Trends Anal. Chem. 2018, 103, 110–118. 10.1016/j.trac.2018.03.016. [DOI] [Google Scholar]
- Block I. D.; Chan L. L.; Cunningham B. T. Large-area Submicron Replica Molding of Porous Low-k Dielectric Films and Application to Photonic Crystal Biosensor Fabrication. Microelectron. Eng. 2007, 84, 603–608. 10.1016/j.mee.2006.12.011. [DOI] [Google Scholar]
- Roda A.; Michelini E.; Zangheri M.; Di Fusco M. D.; Calabria D.; Simoni P. Smartphone-Based Biosensors: A Critical Review and Perspectives. TrAC, Trends Anal. Chem. 2016, 79, 317–325. 10.1016/j.trac.2015.10.019. [DOI] [Google Scholar]
- Wood C. S.; Thomas M. R.; Budd J.; Mashamba-Thompson T. P.; Herbst K.; Pillay D.; Peeling R. W.; Johnson A. M.; McKendry R. A.; Stevens M. M. Taking Connected Mobile-Health Diagnostics of Infectious Diseases to the Field. Nature 2019, 566, 467–474. 10.1038/s41586-019-0956-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cortez N. G.; Cohen I. G.; Kesselheim A. S. FDA Regulation of Mobile Health Technologies. N. Engl. J. Med. 2014, 371, 372–379. 10.1056/NEJMhle1403384. [DOI] [PubMed] [Google Scholar]
- Broughton J. P.; Deng X.; Yu G.; Fasching C. L.; Servellita V.; Singh J.; Miao X.; Streithorst J. A.; Granados A.; Sotomayor-Gonzalez A.; Zorn K.; Gopez A.; Hsu E.; Gu W.; Miller S.; Pan C.-Y.; Guevara H.; Wadford D. A.; Chen J. S.; Chiu C. Y. CRISPR-Cas12-Based Detection of SARS-CoV-2. Nat. Biotechnol. 2020, 1–5. 10.1038/s41587-020-0513-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Moitra P.; Alafeef M.; Dighe K.; Frieman M.; Pan D.. Selective Naked-Eye Detection of SARS-CoV-2 Mediated by N Gene Targeted Antisense Oligonucleotide Capped Plasmonic Nanoparticles. ACS Nano 2020, 10.1021/acsnano.0c03822 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Talukder S.; Gabai G.; Celi P. The Use of Digital Infrared Thermography and Measurement of Oxidative Stress Biomarkers as Tools to Diagnose Foot Lesions in Sheep. Small Ruminant Res. 2015, 127, 80–85. 10.1016/j.smallrumres.2015.04.006. [DOI] [Google Scholar]
- Paraskevaidi M.; Morais C. L.; Lima K. M.; Ashton K. M.; Stringfellow H. F.; Martin-Hirsch P. L.; Martin F. L. Potential of Mid-Infrared Spectroscopy as a Non-Invasive Diagnostic Test in Urine for Endometrial or Ovarian Cancer. Analyst 2018, 143, 3156–3163. 10.1039/C8AN00027A. [DOI] [PubMed] [Google Scholar]
- Scully C. G.; Karaboué A.; Liu W.-M.; Meyer J.; Innominato P. F.; Chon K. H.; Gorbach A. M.; Lévi F. Skin Surface Temperature Rhythms as Potential Circadian Biomarkers for Personalized Chronotherapeutics in Cancer Patients. Interface Focus 2011, 1, 48–60. 10.1098/rsfs.2010.0012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bausells J.; Carrabina J.; Errachid A.; Merlos A. Ion-Sensitive Field-Effect Transistors Fabricated in a Commercial CMOS Technology. Sens. Actuators, B 1999, 57, 56–62. 10.1016/S0925-4005(99)00135-5. [DOI] [Google Scholar]
- Tsai H.-H.; Lin C.-F.; Juang Y.-Z.; Wang I.-L.; Lin Y.-C.; Wang R.-L.; Lin H.-Y. Multiple Type Biosensors Fabricated Using the CMOS BioMEMS Platform. Sens. Actuators, B 2010, 144, 407–412. 10.1016/j.snb.2009.01.061. [DOI] [Google Scholar]
- Syu Y.-C.; Hsu W.-E.; Lin C.-T. Field-Effect Transistor Biosensing: Devices and Clinical Applications. ECS J. Solid State Sci. Technol. 2018, 7, 3196. 10.1149/2.0291807jss. [DOI] [Google Scholar]
- Blanco J. R.; Ferrero F. J.; Campo J. C.; Anton J. C.; Pingarron J. M.; Reviejo A. J.; Manso J. Design of a Low-Cost Portable Potentiostat for Amperometric Biosensors. 2006 IEEE Instrum. Meas. Technol. Conf. 2006, 690–694. 10.1109/IMTC.2006.328670. [DOI] [Google Scholar]
- Loncaric C.; Tang Y.; Ho C.; Parameswaran M. A.; Yu H.-Z. A USB-Based Electrochemical Biosensor Prototype for Point-Of-Care Diagnosis. Sens. Actuators, B 2012, 161, 908–913. 10.1016/j.snb.2011.11.061. [DOI] [Google Scholar]
- Han J.-H.; Lee D.; Chew C. H. C.; Kim T.; Pak J. J. A Multi-Virus Detectable Microfluidic Electrochemical Immunosensor for Simultaneous Detection of H1N1, H5N1, and H7N9 Virus Using ZnO Nanorods for Sensitivity Enhancement. Sens. Actuators, B 2016, 228, 36–42. 10.1016/j.snb.2015.07.068. [DOI] [Google Scholar]
- Li Y.; Hong M.; Lin Y.; Bin Q.; Lin Z.; Cai Z.; Chen G. Highly Sensitive Electrochemical Immunoassay for H1NI Influenza Virus Based on Copper-Mediated Amplification. Chem. Commun. 2012, 48, 6562–6564. 10.1039/c2cc31990j. [DOI] [PubMed] [Google Scholar]
- Layqah L. A.; Eissa S. An Electrochemical Immunosensor for the Corona Virus Associated with the Middle East Respiratory Syndrome Using an Array of Gold Nanoparticle-Modified Carbon Electrodes. Microchim. Acta 2019, 186, 224. 10.1007/s00604-019-3345-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ishikawa F. N.; Chang H.-K.; Curreli M.; Liao H.-I.; Olson C. A.; Chen P.-C.; Zhang R.; Roberts R. W.; Sun R.; Cote R. J.; Thompson M. E.; Zhou C. Label- Free, Electrical Detection of the SARS Virus N-Protein with Nanowire Biosensors Utilizing Antibody Mimics as Capture Probes. ACS Nano 2009, 3, 1219–1224. 10.1021/nn900086c. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Malecka K.; Stachyra A.; Góra-Sochacka A.; Sirko A.; Zagórski-Ostoja W.; Radecka H.; Radecki J. Electrochemical Genosensor Based on Disc and Screen Printed Gold Electrodes for Detection of Specific DNA and RNA Sequences Derived from Avian Influenza Virus H5N1. Sens. Actuators, B 2016, 224, 290–297. 10.1016/j.snb.2015.10.044. [DOI] [Google Scholar]
- Kim Y. T.; Jung J. H.; Choi Y. K.; Seo T. S. A Packaged Paper Fluidic-Based Microdevice for Detecting Gene Expression of Influenza A Virus. Biosens. Bioelectron. 2014, 61, 485–490. 10.1016/j.bios.2014.06.006. [DOI] [PubMed] [Google Scholar]
- Krejcova L.; Hynek D.; Adam V.; Hubalek J.; Kizek R. Electrochemical Sensors and Biosensors for Influenza Detection. Int. J. Electrochem. Sci. 2012, 7, 10779–10801. [Google Scholar]
- Seo G.; Lee G.; Kim M. J.; Baek S.-H.; Choi M.; Ku K. B.; Lee C.-S.; Jun S.; Park D.; Kim H. G.; et al. Detection of COVID-19 Causative Virus (SARS-CoV-2) in Human Nasopharyngeal Swab Specimens Using Field-Effect Transistor-Based Biosensor. ACS Nano 2020, 14, 5135–5142. 10.1021/acsnano.0c02823. [DOI] [PubMed] [Google Scholar]
- Mahari S.; Roberts A.; Shahdeo D.; Gandhi S.. eCovSens-Ultrasensitive Novel In- House Built Printed Circuit Board Based Electrochemical Device for Rapid Detection of nCovid-19. bioRxiv, May 11, 2020. 10.1101/2020.04.24.059204 (accessed on 2020-05-11). [DOI]
- Maier S. A.Plasmonics: Fundamentals and Applications; Springer Science & Business Media: New York, NY, 2007; pp 6–10. [Google Scholar]
- Ligler F. S.; Breimer M.; Golden J. P.; Nivens D. A.; Dodson J. P.; Green T. M.; Haders D. P.; Sadik O. A. Integrating Waveguide Biosensor. Anal. Chem. 2002, 74, 713–719. 10.1021/ac015607s. [DOI] [PubMed] [Google Scholar]
- Socorro-Leránoz A. B.; Santano D.; Del Villar I.; Matias I. R. Trends in the Design of Wavelength-Based Optical Fibre Biosensors (2008–2018). Biosens. Bioelectron.: X 2019, 1, 100015. 10.1016/j.biosx.2019.100015. [DOI] [Google Scholar]
- Rodriguez G. A.; Markov P.; Cartwright A. P.; Choudhury M. H.; Afzal F. O.; Cao T.; Halimi S. I.; Retterer S. T.; Kravchenko I. I.; Weiss S. M. Photonic Crystal Nanobeam Biosensors Based on Porous Silicon. Opt. Express 2019, 27, 9536–9549. 10.1364/OE.27.009536. [DOI] [PubMed] [Google Scholar]
- Ma R.-M.; Oulton R. F. Applications of Nanolasers. Nat. Nanotechnol. 2019, 14, 12–22. 10.1038/s41565-018-0320-y. [DOI] [PubMed] [Google Scholar]
- Cooper M. A. Optical Biosensors in Drug Discovery. Nat. Rev. Drug Discovery 2002, 1, 515–528. 10.1038/nrd838. [DOI] [PubMed] [Google Scholar]
- Kamikawa T. L.; Mikolajczyk M. G.; Kennedy M.; Zhong L.; Zhang P.; Setterington E. B.; Scott D. E.; Alocilja E. C. Pandemic Electrically Active Magnetic Nanoparticles and Surface Plasmon Resonance. IEEE Trans. Nanotechnol. 2012, 11, 88–96. 10.1109/TNANO.2011.2157936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang J. C.; Chang Y.-F.; Chen K.-H.; Su L.-C.; Lee C.-W.; Chen C.-C.; Chen Y.- M. A.; Chou C. Detection of Severe Acute Respiratory Syndrome (SARS) Coronavirus Nucleocapsid Protein in Human Serum Using a Localized Surface Plasmon Coupled Fluorescence Fiber-Optic Biosensor. Biosens. Bioelectron. 2009, 25, 320–325. 10.1016/j.bios.2009.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lu G.; Hu Y.; Wang Q.; Qi J.; Gao F.; Li Y.; Zhang Y.; Zhang W.; Yuan Y.; Bao J.; Zhang B.; Shi Y.; Yan J.; Gao G. F. Molecular Basis of Binding between Novel Human Coronavirus MERS-CoV and Its Receptor CD26. Nature 2013, 500, 227–231. 10.1038/nature12328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Takemura K.; Adegoke O.; Takahashi N.; Kato T.; Li T.-C.; Kitamoto N.; Tanaka T.; Suzuki T.; Park E. Y. Versatility of a Localized Surface Plasmon Resonance-Based Gold Nanoparticle-Alloyed Quantum Dot Nanobiosensor for Immunofluorescence Detection of Viruses. Biosens. Bioelectron. 2017, 89, 998–1005. 10.1016/j.bios.2016.10.045. [DOI] [PubMed] [Google Scholar]
- Qiu G.; Gai Z.; Tao Y.; Schmitt J.; Kullak-Ublick G. A.; Wang J. Dual-Functional Plasmonic Photothermal Biosensors for Highly Accurate Severe Acute Respiratory Syndrome Coronavirus 2 Detection. ACS Nano 2020, 14, 5268–5277. 10.1021/acsnano.0c02439. [DOI] [PubMed] [Google Scholar]
- Bhalla N.; Sathish S.; Galvin C. J.; Campbell R. A.; Sinha A.; Shen A. Q. Plasma- Assisted Large-Scale Nanoassembly of Metal-Insulator Bioplasmonic Mushrooms. ACS Appl. Mater. Interfaces 2018, 10, 219–226. 10.1021/acsami.7b15396. [DOI] [PubMed] [Google Scholar]
- Roether J.; Chu K.-Y.; Willenbacher N.; Shen A. Q.; Bhalla N. Real-Time Monitoring of DNA Immobilization and Detection of DNA Polymerase Activity by a Microfluidic Nanoplasmonic Platform. Biosens. Bioelectron. 2019, 142, 111528. 10.1016/j.bios.2019.111528. [DOI] [PubMed] [Google Scholar]
- Bhalla N.; Fried A. S.; Chu K.-Y.. Nanoplasmonic Instrumentation, Materials, Methods and System Integration. Patent WO/2018/194184, October 25, 2018.
- Rampazzi S.; Danese G.; Leporati F.; Marabelli F. A Localized Surface Plasmon Resonance-Based Portable Instrument for Quick On-Site Biomolecular Detection. IEEE Trans. Instrum. Meas. 2016, 65, 317–327. 10.1109/TIM.2015.2465691. [DOI] [Google Scholar]
- Wei Q.; Qi H.; Luo W.; Tseng D.; Ki S. J.; Wan Z.; Göröcs Z.; Bentolila L. A.; Wu T.-T.; Sun R.; Ozcan A. Fluorescent Imaging of Single Nanoparticles and Viruses on a Smart Phone. ACS Nano 2013, 7, 9147–9155. 10.1021/nn4037706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Neužil P.; Giselbrecht S.; Länge K.; Huang T. J.; Manz A. Revisiting Lab-On-A-Chip Technology for Drug Discovery. Nat. Rev. Drug Discovery 2012, 11, 620–632. 10.1038/nrd3799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Holmes E. C.; Rambaut A. Viral Evolution and the Emergence of SARS Coronavirus. Philos. Trans. R. Soc., B 2004, 359, 1059–1065. 10.1098/rstb.2004.1478. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zeng Q.; Langereis M. A.; van Vliet A. L.; Huizinga E. G.; de Groot R. J. Structure of Coronavirus Hemagglutinin-Esterase Offers Insight into Corona and Influenza Virus Evolution. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 9065–9069. 10.1073/pnas.0800502105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hillisch A.; Pineda L. F.; Hilgenfeld R. Utility of Homology Models in the Drug Discovery Process. Drug Discovery Today 2004, 9, 659–669. 10.1016/S1359-6446(04)03196-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhuo R.; Liu H.; Liu N.; Wang Y. Ligand Fishing: A Remarkable Strategy for Discovering Bioactive Compounds from Complex Mixture of Natural Products. Molecules 2016, 21, 1516. 10.3390/molecules21111516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Scartezzini P.; Speroni E. Review on Some Plants of Indian Traditional Medicine with Antioxidant Activity. J. Ethnopharmacol. 2000, 71, 23–43. 10.1016/S0378-8741(00)00213-0. [DOI] [PubMed] [Google Scholar]
- Lu H. Drug Treatment Options for the 2019-New Coronavirus (2019-nCoV). BioSci. Trends 2020, 14, 69–71. 10.5582/bst.2020.01020. [DOI] [PubMed] [Google Scholar]
- Bhardwaj V. K.; Singh R.; Sharma J.; Rajendran V.; Purohit R.; Kumar S. Identification of Bioactive Molecules from Tea Plant as SARS-CoV-2 Main Protease Inhibitors. J. Biomol. Struct. Dyn. 2020, 1–13. 10.1080/07391102.2020.1766572. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wei H.; Zhang X.; Tian X.; Wu G. Pharmaceutical Applications of Affinity- Ultrafiltration Mass Spectrometry: Recent Advances and Future Prospects. J. Pharm. Biomed. Anal. 2016, 131, 444–453. 10.1016/j.jpba.2016.09.021. [DOI] [PubMed] [Google Scholar]
- Xu N.; Yang H.; Cui M.; Wan C.; Liu S. High-Performance Liquid Chromatography- Electrospray Ionization-Mass Spectrometry Ligand Fishing Assay: A Method for Screening Triplex DNA Binders from Natural Plant Extracts. Anal. Chem. 2012, 84, 2562–2568. 10.1021/ac202796v. [DOI] [PubMed] [Google Scholar]
- Nelson R. W.; Nedelkov D.; Tubbs K. A. Biosensor Chip Mass Spectrometry: A Chip- Based Proteomics Approach. Electrophoresis 2000, 21, 1155–1163. . [DOI] [PubMed] [Google Scholar]
- Huebner A.; Bratton D.; Whyte G.; Yang M.; Demello A. J.; Abell C.; Hollfelder F. Static Microdroplet Arrays: A Microfluidic Device for Droplet Trapping, Incubation and Release for Enzymatic and Cell-Based Assays. Lab Chip 2009, 9, 692–698. 10.1039/B813709A. [DOI] [PubMed] [Google Scholar]
- Chen Y.; Austin R. H.; Sturm J. C. On-Chip Cell Labelling and Washing by Capture and Release Using Microfluidic Trap Arrays. Biomicrofluidics 2017, 11, 054107 10.1063/1.4985771. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lee C.-Y.; Chang C.-L.; Wang Y.-N.; Fu L.-M. Microfluidic Mixing: A Review. Int. J. Mol. Sci. 2011, 12, 3263–3287. 10.3390/ijms12053263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Uzel S. G.; Platt R. J.; Subramanian V.; Pearl T. M.; Rowlands C. J.; Chan V.; Boyer L. A.; So P. T.; Kamm R. D. Microfluidic Device for the Formation of Excitable, Three-Dimensional, Compartmentalized Motor Units. Sci. Adv. 2016, 2, e1501429 10.1126/sciadv.1501429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Manga Y. B.; Ko F.-H.; Yang Y.-S.; Hung J.-Y.; Yang W.-L.; Huang H.-M.; Wu C.-C. Ultra-Fast and Sensitive Silicon Nanobelt Field-Effect Transistor for High-Throughput Screening of Alpha-Fetoprotein. Sens. Actuators, B 2018, 256, 1114–1121. 10.1016/j.snb.2017.10.007. [DOI] [Google Scholar]
- Huang X.; Yu H.; Liu X.; Jiang Y.; Yan M.; Wu D. A Dual-Mode Large-Arrayed CMOS ISFET Sensor for Accurate and High-Throughput pH Sensing in Biomedical Diagnosis. IEEE Trans. Biomed. Eng. 2015, 62, 2224–2233. 10.1109/TBME.2015.2419233. [DOI] [PubMed] [Google Scholar]
- Chen Y.; Wong C. C.; Pui T. S.; Nadipalli R.; Weerasekera R.; Chandran J.; Yu H.; Rahman A. R. CMOS High Density Electrical Impedance Biosensor Array for Tumor Cell Detection. Sens. Actuators, B 2012, 173, 903–907. 10.1016/j.snb.2012.07.024. [DOI] [Google Scholar]
- Ruemmele J. A.; Hall W. P.; Ruvuna L. K.; Van Duyne R. P. A Localized Surface Plasmon Resonance Imaging Instrument for Multiplexed Biosensing. Anal. Chem. 2013, 85, 4560–4566. 10.1021/ac400192f. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Piliarik M.; Vaisocherová H.; Homola J. A New Surface Plasmon Resonance Sensor for High-Throughput Screening Applications. Biosens. Bioelectron. 2005, 20, 2104–2110. 10.1016/j.bios.2004.09.025. [DOI] [PubMed] [Google Scholar]
- Zaytseva N. V.; Montagna R. A.; Lee E. M.; Baeumner A. J. Multi-Analyte Single- Membrane Biosensor for the Serotype-Specific Detection of Dengue Virus. Anal. Bioanal. Chem. 2004, 380, 46–53. 10.1007/s00216-004-2724-9. [DOI] [PubMed] [Google Scholar]
- Schuck P. Reliable Determination of Binding Affinity and Kinetics Using Surface Plasmon Resonance Biosensors. Curr. Opin. Biotechnol. 1997, 8, 498–502. 10.1016/S0958-1669(97)80074-2. [DOI] [PubMed] [Google Scholar]
- Wang Y.; Chen Z.-H. Bioinformatics and Enzymatics Investigation of Trametes Laccase for Optical Biosensing Application. J. Mater. Sci. 2019, 54, 4970–4983. 10.1007/s10853-018-03187-9. [DOI] [Google Scholar]
- Krishnamoorthy G.; Carlen E. T.; van den Berg A.; Schasfoort R. B. Surface Plasmon Resonance Imaging Based Multiplex Biosensor: Integration of Biomolecular Screening, Detection and Kinetics Estimation. Sens. Actuators, B 2010, 148, 511–521. 10.1016/j.snb.2010.04.015. [DOI] [Google Scholar]
- Cooper M. A. Optical Biosensors in Drug Discovery. Nat. Rev. Drug Discovery 2002, 1, 515–528. 10.1038/nrd838. [DOI] [PubMed] [Google Scholar]
- Edmondson R.; Broglie J. J.; Adcock A. F.; Yang L. Three-Dimensional Cell Culture Systems and Their Applications in Drug Discovery and Cell-Based Biosensors. Assay Drug Dev. Technol. 2014, 12, 207–218. 10.1089/adt.2014.573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Vamathevan J.; Clark D.; Czodrowski P.; Dunham I.; Ferran E.; Lee G.; Li B.; Madabhushi A.; Shah P.; Spitzer M.; et al. Applications of Machine Learning in Drug Discovery and Development. Nat. Rev. Drug Discovery 2019, 18, 463–477. 10.1038/s41573-019-0024-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ranamukhaarachchi S. A.; Padeste C.; Dübner M.; Häfeli U. O.; Stoeber B.; Cadarso V. J. Integrated Hollow Microneedle-Optofluidic Biosensor for Therapeutic Drug Monitoring in Sub-Nanoliter Volumes. Sci. Rep. 2016, 6, 29075. 10.1038/srep29075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Aliakbarinodehi N.; Jolly P.; Bhalla N.; Miodek A.; De Micheli G.; Estrela P.; Carrara S. Aptamer-Based Field-Effect Biosensor for Tenofovir Detection. Sci. Rep. 2017, 7, 44409. 10.1038/srep44409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Collet J.-P.; Cuisset T.; Rangé G.; Cayla G.; Elhadad S.; Pouillot C.; Henry P.; Motreff P.; Carrié D.; Boueri Z.; et al. Bedside Monitoring to Adjust Antiplatelet Therapy for Coronary Stenting. N. Engl. J. Med. 2012, 367, 2100–2109. 10.1056/NEJMoa1209979. [DOI] [PubMed] [Google Scholar]
- Neužil P.; Giselbrecht S.; Länge K.; Huang T. J.; Manz A. Revisiting Lab-On-A-Chip Technology for Drug Discovery. Nat. Rev. Drug Discovery 2012, 11, 620–632. 10.1038/nrd3799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dittrich P. S.; Manz A. Lab-On-A-Chip: Microfluidics in Drug Discovery. Nat. Rev. Drug Discovery 2006, 5, 210–218. 10.1038/nrd1985. [DOI] [PubMed] [Google Scholar]
- Almeida S. A.; Arasa E.; Puyol M.; Martinez-Cisneros C. S.; Alonso-Chamarro J.; Montenegro M.; Sales M. G. F. Novel LTCC-Potentiometric Microfluidic Device for Biparametric Analysis of Organic Compounds Carrying Plastic Antibodies as Ionophores: Application to Sulfamethoxazole and Trimethoprim. Biosens. Bioelectron. 2011, 30, 197–203. 10.1016/j.bios.2011.09.011. [DOI] [PubMed] [Google Scholar]
- Sri S.; Dhand C.; Rathee J.; Ramakrishna S.; Solanki P. R. Microfluidic Based Biosensors as Point of Care Devices for Infectious Diseases Management. Sens. Lett. 2019, 17, 4–16. 10.1166/sl.2019.3976. [DOI] [Google Scholar]
- Gray E. R.; Turbé V.; Lawson V. E.; Page R. H.; Cook Z. C.; Ferns R. B.; Nastouli E.; Pillay D.; Yatsuda H.; Athey D.; McKendry R. A. Ultra-Rapid, Sensitive and Specific Digital Diagnosis of HIV with a Dual-Channel SAW Biosensor in a Pilot Clinical Study. NPJ. Digit. Med. 2018, 1, 35. 10.1038/s41746-018-0041-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang G.-J.; Zhang L.; Huang M. J.; Luo Z. H. H.; Tay G. K. I.; Lim E.-J. A.; Kang T. G.; Chen Y. Silicon Nanowire Biosensor for Highly Sensitive and Rapid Detection of Dengue Virus. Sens. Actuators, B 2010, 146, 138–144. 10.1016/j.snb.2010.02.021. [DOI] [Google Scholar]
- Nasseri B.; Soleimani N.; Rabiee N.; Kalbasi A.; Karimi M.; Hamblin M. R. Point- Of-Care Microfluidic Devices for Pathogen Detection. Biosens. Bioelectron. 2018, 117, 112–128. 10.1016/j.bios.2018.05.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Urdea M.; Penny L. A.; Olmsted S. S.; Giovanni M. Y.; Kaspar P.; Shepherd A.; Wilson P.; Dahl C. A.; Buchsbaum S.; Moeller G.; et al. Requirements for High Impact Diagnostics in the Developing World. Nature 2006, 444, 73–79. 10.1038/nature05448. [DOI] [PubMed] [Google Scholar]
- Mao K.; Zhang H.; Yang Z. Can a Paper-Based Device Trace COVID-19 Sources with Wastewater-Based Epidemiology?. Environ. Sci. Technol. 2020, 54, 3733–3735. 10.1021/acs.est.0c01174. [DOI] [PubMed] [Google Scholar]
- Choi P. M.; Tscharke B. J.; Donner E.; O’Brien J. W.; Grant S. C.; Kaserzon S. L.; Mackie R.; O’Malley E.; Crosbie N. D.; Thomas K. V.; Mueller J. F. Wastewater- Based Epidemiology Biomarkers: Past, Present and Future. TrAC, Trends Anal. Chem. 2018, 105, 453–469. 10.1016/j.trac.2018.06.004. [DOI] [Google Scholar]
- O’Bannon D. J.Women in Water Quality: Investigations by Prominent Female Engineers, 1st ed.; Springer: Kansas City, MO, 2019; pp 23–46. [Google Scholar]
- Gao Q. Y.; Chen Y. X.; Fang J. Y. 2019 Novel Coronavirus Infection and Gastrointestinal Tract. J. Dig. Dis. 2020, 21, 125–126. 10.1111/1751-2980.12851. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Holshue M. L.; DeBolt C.; Lindquist S.; Lofy K. H.; Wiesman J.; Bruce H.; Spitters C.; Ericson K.; Wilkerson S.; Tural A.; Diaz G.; Cohn A.; Fox L.; Patel A.; Gerber S. I.; Kim L.; Tong S.; Lu X.; Lindstrom S.; Pallansch M. A.; et al. First Case of 2019 Noveel Coronavirus in the United States. N. Engl. J. Med. 2020, 382, 929–936. 10.1056/NEJMoa2001191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Woelfel R.; Corman V. M.; Guggemos W.; Seilmaier M.; Zange S.; Mueller M. A.. Clinical Presentation and Virological Assessment of Hospitalized Cases of Coronavirus Disease 2019 in a Travel-Associated Transmission Cluster. medRxiv, March 8, 2020. 10.1101/2020.03.05.20030502 (accessed on 2020-05-24). [DOI]
- Young B. E.; Ong S. W. X.; Kalimuddin S.; Low J. G.; Tan S. Y.; Loh J.; Ng O.-T.; Marimuthu K.; Ang L. W.; Mak T. M.; Lau S. K.; Anderson D. E.; Chan K. S.; Tan T. Y.; Ng T. Y.; Cui L.; Said Z.; Kurupatham L.; Chen M. I.-C.; Chan M.; et al. Epidemiologic Features and Clinical Course of Patients Infected with SARS-CoV-2 in Singapore. Jama 2020, 323, 1488–1494. 10.1001/jama.2020.3204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kim J. Y.; Ko J.-H.; Kim Y.; Kim Y.-J.; Kim J.-M.; Chung Y.-S.; Kim H. M.; Han M.-G.; Kim S. Y.; Chin B. S. Viral Load Kinetics of SARS-CoV-2 Infection in First Two Patients in Korea. J. Korean Med. Sci. 2020, 35, e86 10.3346/jkms.2020.35.e86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xu Y.; Li X.; Zhu B.; Liang H.; Fang C.; Gong Y.; Guo Q.; Sun X.; Zhao D.; Shen J.; Zhang H.; Liu H.; Xia H.; Tang J.; Zhang K.; Gong S. Characteristics of Pediatric SARS-CoV-2 Infection and Potential Evidence for Persistent Fecal Viral Shedding. Nat. Med. 2020, 26, 502–505. 10.1038/s41591-020-0817-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Medema G.; Heijnen L.; Elsinga G.; Italiaander R.; Brouwer A.. Presence of SARS- Coronavirus-2 in Sewage. medRxiv, March 30, 2020. 10.1021/acs.estlett.0c00357 (accessed on 2020-05-22). [DOI] [PubMed]
- Wu F.; Xiao A.; Zhang J.; Gu X.; Lee W. L.; Kauffman K.; Hanage W.; Matus M.; Ghaeli N.; Endo N.; Duvallet C.; Moniz K.; Erickson T.; Chai P.; Thompson J.; Alm E.. SARS-CoV-2 Titers in Wastewater Are Higher Than Expected From Clinically Confirmed Cases. medRxiv, April 7, 2020. 10.1101/2020.04.05.20051540 (accessed on 2020-05-24). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wurtzer S.; Marechal V.; Mouchel J.-M.; Moulin L.. Time Course Quantitative Detection of SARS-CoV-2 in Parisian Wastewaters Correlates with COVID-19 Confirmed Cases. medRxiv, April 17, 2020. 10.1101/2020.04.12.2006267 (accessed on 2020-05-23). [DOI] [Google Scholar]
- Ahmed W.; Angel N.; Edson J.; Bibby K.; Bivins A.; O’Brien J. W.; Choi P. M.; Kitajima M.; Simpson S. L.; Li J.; Tscharke B.; Verhagen R.; Smith W. J.; Zaugg J.; Dierens L.; Hugenholtz P.; Thomas K. V.; Mueller J. F. First Confirmed Detection of SARS-CoV-2 in Untreated Wastewater in Australia: A Proof of Concept for the Wastewater Surveillance of COVID-19 in the Community. Sci. Total Environ. 2020, 728, 138764. 10.1016/j.scitotenv.2020.138764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Randazzo W.; Truchado P.; Ferrando E. C.; Simon P.; Allende A.; Sanchez G. SARS-CoV-2 RNA Titers in Wastewater Anticipated COVID-19 Occurrence in a Low Prevalence Area. Water Res. 2020, 181, 115942. 10.1016/j.watres.2020.115942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Corman V. M.; Landt O.; Kaiser M.; Molenkamp R.; Meijer A.; Chu D. K.; Bleicker T.; Brünink S.; Schneider J.; Schmidt M. L.; et al. Detection of 2019 Novel Coronavirus (2019-nCoV) by Real-Time RT-PCR. Eurosurveillance 2020, 25, 2000045. 10.2807/1560-7917.ES.2020.25.3.2000045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang Z.; Xu G.; Reboud J.; Ali S. A.; Kaur G.; McGiven J.; Boby N.; Gupta P. K.; Chaudhuri P.; Cooper J. M. Rapid Veterinary Diagnosis of Bovine Reproductive Infectious Diseases from Semen Using Paper-Origami DNA Microfluidics. ACS Sens. 2018, 3, 403–409. 10.1021/acssensors.7b00825. [DOI] [PubMed] [Google Scholar]
- Liu L.; Yang D.; Liu G. Signal Amplification Strategies for Paper-Based Analytical Devices. Biosens. Bioelectron. 2019, 136, 60–75. 10.1016/j.bios.2019.04.043. [DOI] [PubMed] [Google Scholar]
- Yang Z.; Kasprzyk-Hordern B.; Frost C. G.; Estrela P.; Thomas K. V. Community Sewage Sensors for Monitoring Public Health. Environ. Sci. Technol. 2015, 49, 5845–5846. 10.1021/acs.est.5b01434. [DOI] [PubMed] [Google Scholar]
- Men D.; Zhou J.; Li W.; Leng Y.; Chen X.; Tao S.; Zhang X.-E. Fluorescent Protein Nanowire-Mediated Protein Microarrays for Multiplexed and Highly Sensitive Pathogen Detection. ACS Appl. Mater. Interfaces 2016, 8, 17472–17477. 10.1021/acsami.6b04786. [DOI] [PubMed] [Google Scholar]
- Choi J. R.; Hu J.; Tang R.; Gong Y.; Feng S.; Ren H.; Wen T.; Li X.; Abas W. A. B. W.; Pingguan-Murphy B.; Xu F. An Integrated Paper-Based Sample-To-Answer Biosensor for Nucleic Acid Testing at the Point of Care. Lab Chip 2016, 16, 611–621. 10.1039/C5LC01388G. [DOI] [PubMed] [Google Scholar]
- Yang Z.; Xu G.; Reboud J.; Kasprzyk-Hordern B.; Cooper J. M. Monitoring Genetic Population Biomarkers for Wastewater-Based Epidemiology. Anal. Chem. 2017, 89, 9941–9945. 10.1021/acs.analchem.7b02257. [DOI] [PubMed] [Google Scholar]
- Chen Y.; Chen L.; Deng Q.; Zhang G.; Wu K.; Ni L.; Yang Y.; Liu B.; Wang W.; Wei C.; Yang J.; Ye G.; Cheng Z. The Presence of SARS-CoV-2 RNA in the Feces of COVID-19 Patients. J. Med. Virol. 2020, 92, 833–844. 10.1002/jmv.25825. [DOI] [PubMed] [Google Scholar]
- Nguyen T. H. V.; Lichière J.; Canard B.; Papageorgiou N.; Attoumani S.; Ferron F.; Coutard B. Structure and Oligomerization State of the C-Terminal Region of the Middle East Respiratory Syndrome Coronavirus Nucleoprotein. Acta Crystallogr. Sect. D: Struct. Biol. 2019, 75, 8–15. 10.1107/S2059798318014948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pak J. E.; Sharon C.; Satkunarajah M.; Auperin T. C.; Cameron C. M.; Kelvin D. J.; Seetharaman J.; Cochrane A.; Plummer F. A.; Berry J. D.; Rini J. M. Structural Insights into Immune Recognition of the Severe Acute Respiratory Syndrome Coronavirus S Protein Receptor Binding Domain. J. Mol. Biol. 2009, 388, 815–823. 10.1016/j.jmb.2009.03.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ricagno S.; Coutard B.; Grisel S.; Brémond N.; Dalle K.; Tocque F.; Campanacci V.; Lichière J.; Lantez V.; Debarnot C.; Cambillau C.; Canard B.; Egloff M. P. Crystallization and Preliminary X-Ray Diffraction Analysis of Nsp15 From SARS Coronavirus. Acta Crystallogr., Sect. F: Struct. Biol. Cryst. Commun. 2006, 62, 409–411. 10.1107/S1744309106009407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lawrence R. M.; Conrad C. E.; Zatsepin N. A.; Grant T. D.; Liu H.; James D.; Nelson G.; Subramanian G.; Aquila A.; Hunter M. S.; Liang M.; Boutet S.; Coe J.; Spence J. C.; Weierstall U.; Liu W.; Fromme P.; Cherezov V.; Hogue B. G. Serial Femtosecond X-Ray Diffraction of Enveloped Virus Microcrystals. Struct. Dyn. 2015, 2, 041720 10.1063/1.4929410. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Luo M. Single Crystal X-Ray Diffraction Analysis of Virus Structure and Its Applications in the Development of Pharmaceutical Agents. Crystallogr. Rev. 2015, 21, 103–121. 10.1080/0889311X.2014.957281. [DOI] [Google Scholar]
- Laue M. Electron Microscopy of Viruses. Methods Cell Biol. 2010, 96, 1–20. 10.1016/S0091-679X(10)96001-9. [DOI] [PubMed] [Google Scholar]
- Goldsmith C. S.; Miller S. E. Modern Uses of Electron Microscopy for Detection of Viruses. Clin. Microbiol. Rev. 2009, 22, 552–563. 10.1128/CMR.00027-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Richert-Pöggeler K. R.; Franzke K.; Hipp K.; Kleespies R. G. Electron Microscopy Methods for Virus Diagnosis and High Resolution Analysis of Viruses. Front. Microbiol. 2019, 9, 3255. 10.3389/fmicb.2018.03255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ohnesorge F. M.; Hörber J. K.; Häberle W.; Czerny C. P.; Smith D. P.; Binnig G. AFM Review Study on Pox Viruses and Living Cells. Biophys. J. 1997, 73, 2183–2194. 10.1016/S0006-3495(97)78250-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuznetsov Y. G.; Victoria J. G.; Robinson W. E.; Mcpherson A. Atomic Force Microscopy Investigation of HIV-Infected Lymphocytes Atomic Force Microscopy InInfected Lymphocytes. J. Virol. 2003, 77, 11896–11909. 10.1128/JVI.77.22.11896-11909.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuznetsov Y. G.; McPherson A. Atomic Force Microscopy in Imaging of Viruses and Virus-Infected Cells. Microbiol. Mol. Biol. Rev. 2011, 75, 268–285. 10.1128/MMBR.00041-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Handisurya A.; Gilch S.; Winter D.; Shafti-Keramat S.; Maurer D.; Schätzl H. M.; Kirnbauer R. Vaccination With Prion Peptide-Displaying Papillomavirus-Like Particles Induces Autoantibodies to Normal Prion Protein that Interfere with Pathologic Prion Protein Production in Infected Cells. FEBS J. 2007, 274, 1747–1758. 10.1111/j.1742-4658.2007.05721.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sougrat R.; Bartesaghi A.; Lifson J. D.; Bennett A. E.; Bess J. W.; Zabransky D. J.; Subramaniam S. Electron Tomography of the Contact between T Cells and SIV/HIV- 1: Implications for Viral Entry. PLoS Pathog. 2007, 3, 0571–0581. 10.1371/journal.ppat.0030063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Subramaniam S.; Bartesaghi A.; Liu J.; Bennett A. E.; Sougrat R. Electron Tomography of Viruses. Curr. Opin. Struct. Biol. 2007, 17, 596–602. 10.1016/j.sbi.2007.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Liu J.; Bartesaghi A.; Borgnia M. J.; Sapiro G.; Subramaniam S. Molecular Architecture of Native HIV-1 gp120 Trimers. Nature 2008, 455, 109–113. 10.1038/nature07159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Blancett C. D.; Fetterer D. P.; Koistinen K. A.; Morazzani E. M.; Monninger M. K.; Piper A. E.; Kuehl K. A.; Kearney B. J.; Norris S. L.; Rossi C. A.; Glass P. J.; Sun M. G. Accurate Virus Quantitation Using a Scanning Transmission Electron Microscopy (STEM) Detector in a Scanning Electron Microscope. J. Virol. Methods 2017, 248, 136–144. 10.1016/j.jviromet.2017.06.014. [DOI] [PubMed] [Google Scholar]
- Drosten C.; Günther S.; Preiser W.; van der Werf S. V.; Brodt H. R.; Becker S.; Rabenau H.; Panning M.; Kolesnikova L.; Fouchier R. A.; et al. Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome. N. Engl. J. Med. 2003, 348, 1967–1976. 10.1056/NEJMoa030747. [DOI] [PubMed] [Google Scholar]
- Ksiazek T. G.; Erdman D.; Goldsmith C. S.; Zaki S. R.; Peret T.; Emery S.; Tong S.; Urbani C.; Comer J. A.; Lim W.; et al. A Novel Coronavirus Associated with Severe Acute Respiratory Syndrome. N. Engl. J. Med. 2003, 348, 1953–1966. 10.1056/NEJMoa030781. [DOI] [PubMed] [Google Scholar]
- Gui M.; Liu X.; Guo D.; Zhang Z.; Yin C. C.; Chen Y.; Xiang Y. Electron Microscopy Studies of the Coronavirus Ribonucleoprotein Complex. Protein Cell 2017, 8, 219–224. 10.1007/s13238-016-0352-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pinto D.; Park Y.-J.; Beltramello M.; Walls A. C.; Tortorici M. A.; Bianchi S.; Jaconi S.; Culap K.; Zatta F.; Marco A. D.. et al. Cross-Neutralization of SARS-CoV-2 by a Human Monoclonal SARS-CoV Antibody. Nature 2020, 10.1038/s41586-020-2349-y. [DOI] [PubMed] [Google Scholar]
- Zhu N.; Zhang D.; Wang W.; Li X.; Yang B.; Song J.; Zhao X.; Huang B.; Shi W.; Lu R.; Niu P.; Zhan F.; Ma X.; Wang D.; Xu W.; Wu G.; Gao G. F.; Tan W. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. 10.1056/NEJMoa2001017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cao D.; Gao Y.; Roesler C.; Rice S.; D’Cunha P.; Zhuang L.; Slack J.; Domke M.; Antonova A.; Romanelli S.; Keating S.; Forero G.; Juneja P.; Liang B. Cryo-EM Structure of the Respiratory Syncytial Virus RNA Polymerase. Nat. Commun. 2020, 11, 368. 10.1038/s41467-019-14246-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin Y.; Yan X.; Cao W.; Wang C.; Feng J.; Duan J.; Xie S. Probing the Structure of the SARS Coronavirus Using Scanning Electron Microscopy. Antiviral Ther. 2004, 9, 287–289. [PubMed] [Google Scholar]
- Zhang L.; Lin D.; Sun X.; Rox K.; Hilgenfeld R.. X-Ray Structure of Main Protease of the Novel Coronavirus SARS-CoV-2 Enables Design of α-Ketoamide Inhibitors. bioRxiv, February 20, 2020. 10.1101/2020.02.17.952879 (accessed on 2020-05-23). [DOI] [Google Scholar]
- Baker T.; Johnson J.. Principles of Virus Structure Determination. Structural Biology of Viruses; Chiu W., Burnett R. M., Garcea R. L., Eds.; Oxford University Press: New York, 1997; pp 38–79. [Google Scholar]
- Powell H. R. Molecular Structure from X-Ray Diffraction. Annu. Rep. Prog. Chem., Sect. C: Phys. Chem. 2006, 102, 92–130. 10.1039/b417155c. [DOI] [Google Scholar]
- Molecular Biomethods Handbook, 1st ed.; Walker J. M., Rapley R., Eds.; Humana Press: Totowa, NJ, 2008; pp 29–39. [Google Scholar]
- Papageorgiou N.; Lichière J.; Baklouti A.; Ferron F.; Sévajol M.; Canard B.; Coutard B. Structural Characterization of the N-Terminal Part of the MERS- CoV Nucleocapsid by X-Ray Diffraction and Small-Angle X-Ray Scattering. Acta Crystallogr., Sect. D: Struct. Biol. 2016, 72, 192–202. 10.1107/S2059798315024328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McPherson A.; Larson S. B. A Guide to the Crystallographic Analysis of Icosahedral Viruses. Crystallogr. Rev. 2015, 21, 3–56. 10.1080/0889311X.2014.963572. [DOI] [Google Scholar]
- Krauss I. R.; Merlino A.; Vergara A.; Sica F. An Overview of Biological Macromolecule Crystallization. Int. J. Mol. Sci. 2013, 14, 11643–11691. 10.3390/ijms140611643. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Earl L. A.; Subramaniam S. Cryo-EM of Viruses and Vaccine Design. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 8903–8905. 10.1073/pnas.1609721113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shoemaker S. C.; Ando N. X-Rays in the Cryo-EM Era: Structural Biology’s Dynamic Future. Biochemistry 2018, 57, 277–285. 10.1021/acs.biochem.7b01031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Barty A.; Caleman C.; Aquila A.; Timneanu N.; Lomb L.; White T. A.; Andreasson J.; Arnlund D.; Bajt S.; Barends T. R.; Barthelmess M.; Bogan M. J.; Bostedt C.; Bozek J. D.; Coffee R.; Coppola N.; Davidsson J.; Deponte D. P.; Doak R. B.; Ekeberg T.; et al. Self-Terminating Diffraction Gates Femtosecond X-Ray Nanocrystallography Measurements. Nat. Photonics 2012, 6, 35–40. 10.1038/nphoton.2011.297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Harrison J. S.; Higgins C. D.; O’Meara M. J.; Koellhoffer J. F.; Kuhlman B. A.; Lai J. R. Role of Electrostatic Repulsion in Controlling pH-Dependent Conformational Changes of Viral Fusion Proteins. Structure 2013, 21, 1085–1096. 10.1016/j.str.2013.05.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Perera R.; Khaliq M.; Kuhn R. J. Closing the Door on Flaviviruses: Entry as a Target for Antiviral Drug Design. Antiviral Res. 2008, 80, 11–22. 10.1016/j.antiviral.2008.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Panneels V.; Wu W.; Tsai C. J.; Nogly P.; Rheinberger J.; Jaeger K.; Cicchetti G.; Gati C.; Kick L. M.; Sala L.; et al. Time-Resolved Structural Studies with Serial Crystallography: A New Light on Retinal Proteins. Struct. Dyn. 2015, 2, 041718. 10.1063/1.4922774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sutton G.; Fry E.; Carter L.; Sainsbury S.; Walter T.; Nettleship J.; Berrow N.; Owens R.; Gilbert R.; Davidson A.; Siddell S.; Poon L. L.; Diprose J.; Alderton D.; Walsh M.; Grimes J. M.; Stuart D. I. The nsp9 Replicase Protein of SARS- Coronavirus, Structure and Functional Insights. Structure 2004, 12, 341–353. 10.1016/j.str.2004.01.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Anand K.; Ziebuhr J.; Wadhwani P.; Mesters J. R.; Hilgenfeld R. (3CL pro) Structure: Basis for Design of Anti-SARS Drugs. Science 2003, 300, 1763–1767. 10.1126/science.1085658. [DOI] [PubMed] [Google Scholar]
- Ricagno S.; Egloff M. P.; Ulferts R.; Coutard B.; Nurizzo D.; Campanacci V.; Cambillau C.; Ziebuhr J.; Canard B. Crystal Structure and Mechanistic Determinants of SARS Coronavirus Nonstructural Protein 15 Define an Endoribonuclease Family. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 11892–11897. 10.1073/pnas.0601708103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang L.; Lin D.; Sun X.; Curth U.; Drosten C.; Sauerhering L.; Becker S.; Rox K.; Hilgenfeld R. Crystal Structure of SARS-CoV-2 Main Protease Provides a Basis for Design of Improved α-ketoamide Inhibitors. Science 2020, 412, 409–412. 10.1126/science.abb3405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miller E. J.; Trewby W.; Payam A. F.; Piantanida L.; Cafolla C.; Voïtchovsky K. Sub-Nanometer Resolution Imaging with Amplitude-Modulation Atomic Force Microscopy in Liquid. J. Visualized Exp. 2016, 1–10. 10.3791/54924. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Novoselov K. S.; Jiang D.; Schedin F.; Booth T. J.; Khotkevich V. V.; Morozov S. V.; Geim A. K. Two-Dimensional Atomic Crystals. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 10451–10453. 10.1073/pnas.0502848102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McLean R. S.; Doyle M.; Sauer B. B. High-Resolution Imaging of Ionic Domains and Crystal Morphology in Ionomers Using AFM Techniques. Macromolecules 2000, 33, 6541–6550. 10.1021/ma000464h. [DOI] [Google Scholar]
- Payam A. F.; Ramos J. R.; Garcia R. Molecular and Nanoscale Compositional Contrast of Soft Matter in Liquid: Interplay between Elastic and Dissipative Interactions. ACS Nano 2012, 6, 4663–4670. 10.1021/nn2048558. [DOI] [PubMed] [Google Scholar]
- Amo C. A.; Perrino A. P.; Payam A. F.; Garcia R. Mapping Elastic Properties of Heterogeneous Materials in Liquid with Angstrom-Scale Resolution. ACS Nano 2017, 11, 8650–8659. 10.1021/acsnano.7b04381. [DOI] [PubMed] [Google Scholar]
- Gerber C.; Lang H. P. How the Doors to the Nanoworld Were Opened. Nat. Nanotechnol. 2006, 1, 3–5. 10.1038/nnano.2006.70. [DOI] [PubMed] [Google Scholar]
- Payam A. F.; Martin-Jimenez D.; Garcia R. Force Reconstruction from Tapping Mode Force Microscopy Experiments. Nanotechnology 2015, 26, 185706. 10.1088/0957-4484/26/18/185706. [DOI] [PubMed] [Google Scholar]
- Freeman M. R.; Choi B. C. Advances in Magnetic Microscopy. Science 2001, 294, 1484–1488. 10.1126/science.1065300. [DOI] [PubMed] [Google Scholar]
- Belianinov A.; Vasudevan R.; Strelcov E.; Steed C.; Yang S. M.; Tselev A.; Jesse S.; Biegalski M.; Shipman G.; Symons C.; Borisevich A.; Archibald R.; Kalinin S. Big Data and Deep Data in Scanning and Electron Microscopies: Deriving Functionality from Multidimensional Data Sets. Adv. Struct. Chem. Imaging 2015, 1, 6. 10.1186/s40679-015-0006-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Warren S. C.; Voïtchovsky K.; Dotan H.; Leroy C. M.; Cornuz M.; Stellacci F.; Hébert C.; Rothschild A.; Grätzel M. Identifying Champion Nanostructures for Solar Water-Splitting. Nat. Mater. 2013, 12, 842–849. 10.1038/nmat3684. [DOI] [PubMed] [Google Scholar]
- Dufrêne Y. F.; Ando T.; Garcia R.; Alsteens D.; Martinez-Martin D.; Engel A.; Gerber C.; Müller D. J. Imaging Modes of Atomic Force Microscopy for Application in Molecular and Cell Biology. Nat. Nanotechnol. 2017, 12, 295–307. 10.1038/nnano.2017.45. [DOI] [PubMed] [Google Scholar]
- Rosso G.; Liashkovich I.; Shahin V. In Situ Investigation of Interrelationships between Morphology and Biomechanics of Endothelial and Glial Cells and their Nuclei. Adv. Sci. 2019, 6, 1801638. 10.1002/advs.201801638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang H.; Dobryden I.; Thorén P. A.; Ejenstam L.; Pan J.; Fielden M. L.; Haviland D. B.; Claesson P. M. Local Surface Mechanical Properties of PDMS- Silica Nanocomposite Probed with Intermodulation AFM. Compos. Sci. Technol. 2017, 150, 111–119. 10.1016/j.compscitech.2017.07.013. [DOI] [Google Scholar]
- Martinez-Torres C.; Arneodo A.; Streppa L.; Argoul P.; Argoul F. Passive Microrheology of Soft Materials with Atomic Force Microscopy: A Wavelet-Based Spectral Analysis. Appl. Phys. Lett. 2016, 108, 034102 10.1063/1.4940220. [DOI] [Google Scholar]
- Laperrousaz B.; Berguiga L.; Nicolini F. E.; Martinez-Torres C.; Arneodo A.; Satta V. M.; Argoul F. Revealing Stiffening and Brittling of Chronic Myelogenous Leukemia Hematopoietic Primary Cells through Their Temporal Response to Shear Stress. Phys. Biol. 2016, 13, 03LT01 10.1088/1478-3975/13/3/03LT01. [DOI] [PubMed] [Google Scholar]
- Baumann K. N.; Piantanida L.; García-Nafría J.; Sobota D.; Voïtchovsky K.; Knowles T. P.; Hernández-Ainsa S. Coating and Stabilization of Liposomes by Clathrin-Inspired DNA Self-Assembly. ACS Nano 2020, 14, 2316–2323. 10.1021/acsnano.9b09453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Payam A. F.; Payton O.; Picco L.; Moore S.; Martin T.; Warren A. D.; Mostafavi M.; Knowles D. Development of Fatigue Testing System for In-Situ Observation of Stainless Steel 316 by HS-AFM & SEM. Int. J. Fatigue 2019, 127, 1–9. 10.1016/j.ijfatigue.2019.05.015. [DOI] [Google Scholar]
- Engel A.; Müller D. J. Observing Single Biomolecules at Work with the Atomic Force Microscope. Nat. Struct. Biol. 2000, 7, 715–718. 10.1038/78929. [DOI] [PubMed] [Google Scholar]
- Garcia R.; Perez R. Dynamic Atomic Force Microscopy Methods. Surf. Sci. Rep. 2002, 47, 197–301. 10.1016/S0167-5729(02)00077-8. [DOI] [Google Scholar]
- Putman C. A. J.; Van der Werf K. O.; De Grooth B. G.; Van Hulst N. F.; Greve J. Tapping Mode Atomic Force Microscopy in Liquid. Appl. Phys. Lett. 1994, 64, 2454–2456. 10.1063/1.111597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hinterdorfer P.; Dufrêne Y. F. Detection and Localization of Single Molecular Recognition Events Using Atomic Force Microscopy. Nat. Methods 2006, 3, 347–355. 10.1038/nmeth871. [DOI] [PubMed] [Google Scholar]
- Oesterhelt F.; Oesterhelt D.; Pfeiffer M.; Engel A.; Gaub H. E.; Müller D. J. Unfolding Pathways of Individual Bacteriorhodopsins. Science 2000, 288, 143–146. 10.1126/science.288.5463.143. [DOI] [PubMed] [Google Scholar]
- Roos W. H.; Bruinsma R.; Wuite G. J. Physical Virology. Nat. Phys. 2010, 6, 733–743. 10.1038/nphys1797. [DOI] [Google Scholar]
- Kufer S. K.; Puchner E. M.; Gumpp H.; Liedl T.; Gaub H. E. Single-Molecule Cut-And-Paste Surface Assembly. Science 2008, 319, 594–596. 10.1126/science.1151424. [DOI] [PubMed] [Google Scholar]
- Müller D. J.; Dufrêne Y. F. Atomic Force Microscopy as a Multifunctional Molecular Toolbox in Nanobiotechnology. Nanoscience and Technology: A Collection of Reviews from Nature Journals 2009, 269–277. 10.1142/9789814287005_0028. [DOI] [PubMed] [Google Scholar]
- Dufrêne Y. F.; Martínez-Martín D.; Medalsy I.; Alsteens D.; Müller D. J. Multiparametric Imaging of Biological Systems by Force-Distance Curve-Based AFM. Nat. Methods 2013, 10, 847–854. 10.1038/nmeth.2602. [DOI] [PubMed] [Google Scholar]
- Hinterdorfer P.; Baumgartner W.; Gruber H. J.; Schilcher K.; Schindler H. Detection and Localization of Individual Antibody-Antigen Recognition Events by Atomic Force Microscopy. Proc. Natl. Acad. Sci. U. S. A. 1996, 93, 3477–3481. 10.1073/pnas.93.8.3477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Grandbois M.; Dettmann W.; Benoit M.; Gaub H. E. Affinity Imaging of Red Blood Cells Using an Atomic Force Microscope. J. Histochem. Cytochem. 2000, 48, 719–724. 10.1177/002215540004800516. [DOI] [PubMed] [Google Scholar]
- Garcia R.; Herruzo E. T. The Emergence of Multifrequency Force Microscopy. Nat. Nanotechnol. 2012, 7, 217–226. 10.1038/nnano.2012.38. [DOI] [PubMed] [Google Scholar]
- Ando T. High-Speed Atomic Force Microscopy and Its Future Prospects. Biophys. Rev. 2018, 10, 285–292. 10.1007/s12551-017-0356-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Damircheli M.; Payam A. F.; Garcia R. Optimization of Phase Contrast in Bimodal Amplitude Modulation AFM. Beilstein J. Nanotechnol. 2015, 6, 1072–1081. 10.3762/bjnano.6.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin S.; Lee C.-K.; Lee S.-Y.; Kao C.-L.; Lin C.-W.; Wang A.-B.; Hsu S.-M.; Huang L.-S. Surface Ultrastructure of SARS Coronavirus Revealed by Atomic Force Microscopy. Cell. Microbiol. 2005, 7, 1763–1770. 10.1111/j.1462-5822.2005.00593.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Aznar M.; Roca-Bonet S.; Reguera D. Viral Nanomechanics with a Virtual Atomic Force Microscope. J. Phys.: Condens. Matter 2018, 30, 264001. 10.1088/1361-648X/aac57a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Limanskaya O. Y. Bioinformatic Analysis of Inverted Repeats of Coronaviruses Genome. Biopolim. Kletka 2009, 25, 307–314. 10.7124/bc.0007EA. [DOI] [Google Scholar]
- Ng M. L.; Lee J. W.; Leong M. L.; Ling A. E.; Tan H. C.; Ooi E. E. Topographic Changes in SARS Coronavirus-Infected Cells During Late Stages of Infection. Emerging Infect. Dis. 2004, 10, 1907–1914. 10.3201/eid1011.040195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Piantanida L.; Payam A. F.; Zhong J.; Voitchovsky K. Nanoscale Mapping of the Directional Flow Patterns at Liquid-Solid Interfaces. Phys. Rev. Appl. 2020, 13, 064003 10.1103/PhysRevApplied.13.064003. [DOI] [Google Scholar]
- Yang J.; Petitjean S.; Derclaye S.; Koehler M.; Zhang Q.; Dumitru A. C.; Soumillion P.; Alsteens D.. Molecular Interaction and Inhibition of SARS-CoV-2 Binding to the ACE2 Receptor. 2020. ResearchSquare. https://www.researchsquare.com/article/rs-30468/v1 (accessed on 2020-05-25). [DOI] [PMC free article] [PubMed]
- Malkin A. J.; Plomp M.; McPherson A.. Unraveling the Architecture of Viruses by High-Resolution Atomic Force Microscopy. DNA Viruses; Humana Press: New Jersey, 2005; Vol. 292, pp 85–108. [DOI] [PubMed] [Google Scholar]
- Iyer M. A.; Oza G.; Velumani S.; Maldonado A.; Romero J.; de L Munoz M.; Sridharan M.; Asomoza R.; Yi J. Scanning Fluorescence-Based Ultrasensitive Detection of Dengue Viral DNA on ZnO Thin Films. Sens. Actuators, B 2014, 202, 1338–1348. 10.1016/j.snb.2014.06.005. [DOI] [Google Scholar]
- Pasquina-Lemonche L.; Burns J.; Turner R. D.; Kumar S.; Tank R.; Mullin N.; Wilson J. S.; Chakrabarti B.; Bullough P. A.; Foster S. J.; Hobbs J. K. The Architecture of the Gram-Positive Bacterial Cell Wall. Nature 2020, 582, 294–297. 10.1038/s41586-020-2236-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Evans R.; McNamee M.; Guy O. Ethics, Nanobiosensors and Elite Sport: The Need for a New Governance Framework. Sci. Eng. Ethics 2017, 23, 1487–1505. 10.1007/s11948-016-9855-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spagnolo A. G.; Daloiso V. Outlining Ethical Issues in Nanotechnologies. Bioethics 2009, 23, 394–402. 10.1111/j.1467-8519.2007.00623.x. [DOI] [PubMed] [Google Scholar]
- Doke S. K.; Dhawale S. C. Alternatives to Animal Testing: A Review. Saudi Pharm. J. 2015, 23, 223–229. 10.1016/j.jsps.2013.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wilkinson J. M.Need for Alternative Testing Methods and Opportunities for Organ- On-A-Chip Systems. Organ-On-A-Chip: Engineered Microenvironments for Safety and Efficacy Testing, 1st ed.; Academic Press: London, 2020; pp 1–11. [Google Scholar]
- Morales-Narváez E.; Dincer C. The Impact of Biosensing in a Pandemic Outbreak: COVID-19. Biosens. Bioelectron. 2020, 163, 112274. 10.1016/j.bios.2020.112274. [DOI] [PMC free article] [PubMed] [Google Scholar]