The Polymerase Chain Reaction Combined With Quantum Dot Fluorescence Analysis Method for Rapid Detection of Multiple Urinary Tract Infection Pathogens in Children

Chunling Li; Huali Yin; Pengfei Yu; Qian Meng; Qianwen Ye; Fei Zhao; Chuanqing Wang

doi:10.1002/jcla.70177

. 2026 Mar 8;40(6):e70177. doi: 10.1002/jcla.70177

The Polymerase Chain Reaction Combined With Quantum Dot Fluorescence Analysis Method for Rapid Detection of Multiple Urinary Tract Infection Pathogens in Children

Chunling Li ¹, Huali Yin ², Pengfei Yu ², Qian Meng ², Qianwen Ye ², Fei Zhao ², Chuanqing Wang ^1,^3,^✉

PMCID: PMC13097366 PMID: 41797429

ABSTRACT

Background

Identifying the pathogens responsible for urinary tract infections (UTIs) using urine culture methods is time‐consuming, and delayed diagnosis significantly impacts patient outcomes.

Methods

We developed a novel platform that combines PCR amplification products with quantum dot fluorescence for hybridization–Polymerase Chain Reaction Combined with Quantum Dot Fluorescence Analysis (PCRQDFA), enabling rapid and efficient detection of pathogenic bacteria in urine. To evaluate the performance of this technique, 294 urine samples from children with UTI were collected and tested using PCR–QDFA and traditional culture methods.

Results

Compared to culture, the consistency rate of PCR–QDFA was 86.73%, with a sensitivity of 86.98% and a specificity of 86.4%. For common pathogens such as Enterococcus faecalis , Escherichia coli , Klebsiella pneumoniae, Enterococcus faecium, Proteus mirabilis, and Enterobacter cloacae , the consistency rates exceeded 95%.

Conclusion

The PCR–QDFA assay has proven to be a reliable tool for the rapid diagnosis of urinary pathogens in children with suspected urinary tract infections.

Keywords: children, diagnostic performance；PCR—QDFA, quantum dots, urinary tract infection

In this study, we developed a novel pathogen detection platform by combining PCR technology with quantum dot fluorescence materials, aiming to improve the speed and accuracy of urinary tract infection (UTI) pathogen detection. We collected 294 clinical samples to evaluate the performance of the platform. The results demonstrated that the platform exhibited good sensitivity and specificity while significantly reducing diagnostic time for patients. This technological platform holds significant clinical value and application potential for the early diagnosis and treatment of UTIs.

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1. Introduction

Urinary tract infection (UTI) refers to an infection that affects any part of the urinary system, typically caused by bacteria [1]. Research has shown that up to 8% of children experience at least one UTI between the ages of 1 month and 11 years, and as many as 30% of infants and children will experience recurrent infections within 6 to 12 months after their first UTI [2, 3]. The symptoms of UTIs in children are often less noticeable than those in adults and tend to present as nonspecific signs, such as fever, irritability, poor appetite, or frequent urination, which makes early diagnosis and treatment more challenging. Without prompt identification of the pathogens and appropriate treatment, UTIs can result in chronic kidney damage, which may have long‐term effects on a child's growth, development, and overall health condition [4]. The incidence of urinary tract infections is also high among women, with around 50% of women experiencing at least one UTI in their lifetime [5]. Therefore, rapid and accurate identification of pathogens is crucial for optimizing clinical treatment and tailoring personalized medication.

Urine culture is considered the gold standard for diagnosing urinary tract infections, while the main limitation is that it typically takes 3 to 5 days to yield results [6]. Additionally, certain sexually transmitted pathogens require specialized media and specific incubation conditions, making the process more challenging and time‐consuming [7]. With the rapid development of molecular diagnostic technologies, numerous studies are exploring the use of advanced molecular biology techniques to replace traditional culture methods, such as mass spectrometry, PCR, and next‐generation sequencing. These technologies have the potential to significantly improve detection efficiency and the accuracy of clinical diagnoses [8, 9, 10]. However, these methods often require complex sample preprocessing, and the identification process can take up to 24 h. Consequently, there is an urgent need to develop a simple, cost‐effective, rapid, and highly specific detection method capable of effectively identifying common pathogens in the urine of UTI patients.

Quantum dot gene chip technology utilizes nanometer‐scale semiconductor crystals that emit fluorescence signals at distinct wavelengths upon exposure to excitation light. This unique optical property improves the sensitivity and resolution of pathogen detection [11]. Previous studies have demonstrated that quantum dot fluorescence technology can detect pathogens such as Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa quickly and accurately [12, 13]. Therefore, combining quantum dot fluorescence technology with PCR enables efficient and rapid pathogen detection, as the fluorescence signals from quantum dots provide more precise and reliable results.

In this study, we aimed to develop a novel technique: polymerase chain reaction combined with quantum dot fluorescence Analysis (PCR—QDFA) and to evaluate its performance.

2. Materials and Methods

2.1. Samples

A total of 294 clinical urine samples were collected from patients suspected of or diagnosed with a UTI at the Children's Hospital of Fudan University, between January 2024 and October 2024. All samples were cultured and identified using matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS). This study was approved by the ethics committee prior to its initiation. Urine samples were stored at −80°C for subsequent experiments.

2.2. Primers and Probes

Species‐specific PCR primers targeting the conserved sequences of each pathogen were designed using DNASTAR and Primer 6.0 software. Based on sequence variations across different genes and following the principle of base complementarity, Primer 6.0 software was used to design species‐specific oligonucleotide probes targeting the gene fragments of the target pathogens. All oligonucleotide probes were labeled with amino groups at the 3′ end and immobilized at predefined positions on a nylon membrane via chemical bonding, forming detection strips. All primers and probes were synthesized by Shanghai Generay Biotech Co. Ltd. (Table S1).

2.3. PCR‐QDFA Hybridization System

We developed a PCR‐QDFA method. The PCR amplicons are first hybridized to probes stably on the membrane surface. Subsequently, biotin combines with streptavidin‐conjugated quantum dots to form a stable complex. Finally, a fluorescence detection instrument is used to observe the light signals at various positions on the detection membrane strip to determine whether the probe has successfully hybridized with the PCR product, thereby indicating whether the sample contains relevant pathogens (Figure 1).

The PCR–quantum dot fully automated nucleic acid hybridization system operates in coordination with a programmable logic controller (PLC) and a temperature control module to perform hybridization, membrane washing, incubation, and imaging (Figure 2A). Prior to hybridization, the instrument automatically heats to the preset temperature. Once stabilized, Solution A is added, followed by preheating and denaturation of the PCR products. Hybridization is then performed. Following hybridization, Solutions B, C, and D are applied sequentially for washing, incubation, and a final wash step (Figure 2B). The DNA microarray in the hybridization zone comprises a nylon membrane with a high surface density of carboxyl groups. Following activation with 16% carbodiimide hydrochloride (EDC), the membrane is coupled to the C6 amine‐modified specific probes, thereby immobilizing the probes stably on the membrane surface. The PCR products first hybridize with the probe molecules on the detection membrane, then bind to streptavidin conjugated with biotin and quantum dots. Subsequently, a fluorescence detection instrument is used to detect the presence of light signals at predefined sites, thereby determining whether the PCR products contain the target pathogen. Finally, images are captured to document the experimental results for subsequent analysis (Figure 2C). The right‐side structure of the hybridization instrument includes a power switch, USB port, leakage protection switch, and power filter, which are primarily used to control the device's operation, facilitate data connection, and ensure electrical safety, thereby ensuring the stability and safety of the instrument during use (Figure 2D). Both the instrument and associated software were manufactured by Hangzhou Qianji Biotechnology Co. Ltd.

PCR–qdfa fully automated nucleic acid molecular hybridization system. (A) Schematic diagram of the overall hybridization system. (B) Structural diagram of the left side of the system. (C) Reaction zone of the system. (D) Structural diagram of the right side of the system.

2.4. PCR—QDFA Method Workflow

Midstream urine samples (10–20 mL) were collected from patients using sterile containers. If immediate testing was not feasible, samples were stored at ≤− 18°C to avoid cross‐contamination.

Sample Pre‐treatment and Nucleic Acid Extraction: The urine samples were inverted and mixed ten times. A 200 μL aliquot of clarified midstream urine and corresponding controls was transferred into a 1.5 mL centrifuge tube, and 1 μL of lyticase was then added. The mixture was incubated in a 30°C water bath for 30 min, after which DNA extraction was performed.

PCR Amplification: 4 μL of DNA from the sample to be tested and the DNA from the control were transferred into the prepared amplification reaction mixture. Amplification Process: UNG enzyme reaction was performed at 50°C for 2 min, followed by pre‐denaturation at 95°C for 5 min. The touchdown PCR steps are as follows: 95°C for 30 s, 60°C for 30 s, and 72°C for 30 s (10 cycles, reducing the temperature by 1°C every 2 cycles). The amplification process is as follows: 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s.

Hybridization Detection: Each membrane strip, labeled with its sample number, was placed into the corresponding hybridization box. 12.5 μL of denaturation reagent was added to the PCR product, centrifuged briefly at low speed, and incubated at room temperature for 10 min. Then, another 12.5 μL of denaturation reagent was added and another brief centrifugation at low speed was performed. The denatured PCR product was transferred into the corresponding membrane strip box. When the instrument prompted for the addition of the incubation solution, the freshly prepared incubation and wash solutions were loaded into the respective positions in the reagent area and then automatic hybridization was performed.

Imaging System: After the instrument completes its run, image analysis was performed using the gene chip data analysis software. Pathogen species were identified based on the locations of the colorimetric signals, and pathogen concentration was determined by the fluorescence intensity.

2.5. Statistical Methods

Statistical analysis was performed using SPSS 27.0 software. The sensitivity and specificity of PCR—QDFA were calculated. The Kappa test was used to evaluate the consistency between the PCR–QDFA and urine culture results. A Kappa value < 0.4 was considered poor or fair; 0.4–0.8 was considered moderate to strong; and > 0.8 was considered very strong.

3. Results

3.1. Construction of the PCR–Quantum Dot Fluorescence Detection Platform

We designed and developed a DNA microarray for pathogen detection (Figure 3A). By analyzing the distinct colorimetric signals at the hybridization sites within the DNA microarray, we were able to identify the types of pathogens present. To improve the accuracy of clinical testing and reduce the risk of false‐negative results, we incorporated an internal control (IC) and a color control (CC). The developed platform successfully detected 19 distinct pathogens (Figure 3B).

Application of the PCR–quantum dot fluorescence detection method. (A) Design of the DNA microarray. The following pathogens were detected: ECL ( *Enterobacter cloacae* ), KPN ( *Klebsiella pneumoniae* ), PM ( *Proteus mirabilis* ), SAP ( *Staphylococcus saprophyticus* ), SEP ( *Staphylococcus epidermidis* ), SPY ( *Streptococcus pyogenes* ), SGC ( *Streptococcus gallolyticus* ), CA ( *Candida albicans* ), CFR ( *Citrobacter freundii* ), ECO ( *Escherichia coli* ), SAU ( *Staphylococcus aureus* ), EFM ( *Enterococcus faecium* ), EFA ( *Enterococcus faecalis* ), PAE ( *Pseudomonas aeruginosa* ), ABA ( *Acinetobacter baumannii* ), CT ( *Chlamydia trachomatis* ), NG ( *Neisseria gonorrhoeae* ), UU ( *Ureaplasma urealyticum* ), MG ( *Mycoplasma genitalium* ), internal control (IC), and color control (CC). (B) PCR–quantum dot hybridization results for each pathogen.

3.2. Hybridization Condition Optimization

This study comprehensively optimized the key experimental conditions for quantum dot hybridization, including the formulations of solutions A and B, hybridization time, membrane washing durations, hybridization and washing temperatures, incubation solution formulation, incubation time, and washing durations. The optimal concentration for solution A was found to be 2 × SSC + 0.1% SDS, while the optimal concentration for solution B was 0.5 × SSC + 0.1% SDS. For hybridization time, 1 h was determined to be the optimal duration, offering a balance between strong fluorescence signals and minimizing overall experimental time. The washing time was set to 15 min, effectively removing non‐specific bindings while maintaining signal strength. The hybridization and washing temperatures were optimized to 48°C, where the hybridization signal was strongest and no non‐specific reactions were observed. The optimal concentration of quantum dot‐conjugated streptavidin in the incubation solution was 1 μL, with an incubation time of 30 min and a washing time of 5 min. These conditions provided a strong signal intensity while also reducing costs and experimental time. The optimized conditions significantly improved the sensitivity and specificity of quantum dot hybridization (Tables S2–S11).

3.3. Positive Judgment Value

Clinical negative samples were collected and tested using this product to confirm their negative status, which was then used as a negative matrix standard. These negative matrix standards were subsequently used to perform gradient dilutions of purchased standardized bacterial cultures, generating simulated samples containing pathogens at concentrations of 1.0 × 10⁵ CFU/mL, 1.0 × 10⁴ CFU/mL, and 1.0 × 10³ CFU/mL. These simulated samples were then tested under optimal reaction conditions. Each sample was tested in duplicate, and the resulting values were analyzed using software to determine a specific range of readings. Through the optimization of the system and other conditions, the positive judgment value for each pathogen at the specified concentrations was determined. Additionally, using the Gene Chip Analysis System V1.0, the membrane strip results for clinical samples were analyzed to establish the preliminary positive cutoff value for PCR‐QDFA. To further optimize the positive cutoff value, 50 known positive and negative pooled samples were collected. Each sample underwent three parallel tests using a single batch of reagents. The true positive rate (sensitivity) and false positive rate (1‐specificity) were calculated at different thresholds, and ROC curves were plotted. By selecting the point with the maximum distance from the diagonal line on the ROC curve, we determined the optimal PCR‐QDFA positive cutoff value (Table 1).

TABLE 1.

Positive Threshold Values for Each Detected Pathogen.

Detection target	Concentration	Positive judgment value
ECL	1.0 × 10⁴ CFU/mL	1
KPN	1.0 × 10⁴ CFU/mL	0.8
PM	1.0 × 10⁴ CFU/mL	0.7
SAP	1.0 × 10⁴ CFU/mL	1
SEP	1.0 × 10⁴ CFU/mL	1.2
SPY	1.0 × 10⁴ CFU/mL	1
SGC	1.0 × 10⁴ CFU/mL	1
CA	1.0 × 10³ CFU/mL	0.8
CFR	1.0 × 10⁴ CFU/mL	1.2
ECO	1.0 × 10⁴ CFU/mL	0.7
SAU	1.0 × 10⁴ CFU/mL	0.8
EFM	1.0 × 10⁴ CFU/mL	0.8
EFA	1.0 × 10⁴ CFU/mL	0.8
PAE	1.0 × 10⁴ CFU/mL	0.5
ABA	1.0 × 10⁴ CFU/mL	1.1
CT	1.0 × 10³copies/mL	0.5
NG	1.0 × 10³ CFU/mL	0.5
UU	1.0 × 10⁴CCU/mL	0.5
MG	1.0 × 10²copies/mL	0.5

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3.4. Minimum Detection Limit of PCR‐QDFA

A study on the minimum detection limit was conducted using genomic or recombinant plasmids from 19 target genes. Each target was tested at three concentration gradients, with each concentration repeated 20 times. Testing was performed using three batches of the product to determine the stable minimum detection limits for PCR‐QDFA: UU at 10⁴ CCU/mL, CT at 10³ copies/mL, NG at 10³ CFU/mL, MG at 100 copies/μL; SAU and PAE at 0.1 pg/μL; SAP, ECL, PM, EFM, CA, KPN, SGC, CFR, and ECO: 0.05 pg/μL; SPY: 0.02 pg/μL; SEP: 0.01 pg/μL; ABA and EFA: 0.005 pg/μL.

3.5. Clinical Performance Validation

Using urine culture results as the reference standard, the overall concordance rate of the PCR‐QDFA method was calculated to be 86.73%, with a sensitivity of 86.98%, a specificity of 86.4%, and a Kappa value of 0.72. The PCR–QDFA results were generally consistent with those of the culture method (Table 2).

TABLE 2.

Detection Results of 294 Clinical Samples.

		Urine culture		Total
		Positive	Negative	Total
PCR‐QDFA	Positive	147	17	164
PCR‐QDFA	Negative	22	108	130
Total		169	125	294

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3.6. Performance of PCR–QDFA in Identifying Individual Pathogens

The 294 urine samples included 169 positive and 125 negative samples. After excluding pathogens outside the detection range of PCR‐QDFA, 146 pathogens were cultured from the positive urine samples, and 139 of these pathogens (95.21%) were successfully identified by PCR–QDFA. Additionally, 9 pathogens (6.16%) were not detected by PCR–QDFA. The results showed that the sensitivity of PCR–QDFA for EFA was 95.16%, for ECO was 95.12%, and for KPN, ECL, CFR, EFM, PAE, CA, and PM, the sensitivity was 100% when compared to urine culture results (Table 3).

TABLE 3.

PCR–QDFA Method for Identifying Single Pathogens within the Target Range.

Detection target	Number of samples
Detection target	+/+	+/−	−/+	Sensitivity
EFA	59	3	9	95.16%
ECO	39	2	2	95.12%
KPN	14	0	0	100.00%
ECL	8	0	0	100.00%
CFR	6	0	1	100.00%
EFM	5	0	1	100.00%
PAE	4	0	1	100.00%
CA	2	0	0	100.00%
PM	2	0	1	100.00%
Total	139	5	15	86.98%

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Note: +/+ represents the number of samples that were positive by both urine culture and PCR–QDFA; +/− represents the number of samples that were positive by urine culture but negative by PCR–QDFA; −/+ represents the number of samples that were negative by urine culture but positive by PCR–QDFA.

4. Discussion

Currently, the traditional methods used clinically to detect pathogens causing urinary tract infections (UTIs) include urine culture, PCR, urine test strips, and microscopic examination. The traditional urine culture method is widely used; however, it is time‐consuming, prone to contamination, and may have relatively low detection rates for certain pathogens [14, 15]. Culture methods rely on the growth and multiplication of pathogens in suitable environments, with detection limits typically ranging from 10³ to 10⁴ CFU/mL. This range is influenced by multiple factors, including the pathogen's growth capacity, survival rate, and the characteristics of the selected culture medium [15, 16, 17]. Therefore, it is difficult to detect pathogens using traditional culture methods when pathogen quantities in samples are low. However, the PCR method directly detects the pathogen's genetic material rather than relying on its growth. This enables the method to identify pathogen DNA at concentrations far below the detection limits of traditional culture methods, making it particularly effective for detecting early and low‐concentration infections [18]. Additionally, urine test strips based on chemical reactions are widely used due to their simplicity and low cost. These strips react with specific components in urine, changing color to indicate potential infections. However, urine test strips exhibit low sensitivity and specificity and are susceptible to interference from urine composition, medications, or sample handling, leading to false positives or false negatives [19]. Urinalysis dipsticks are prone to false‐negative results when pathogen loads are low or during the early stages of infection [20].

Quantum dots, low‐toxicity and cost‐effective nanomaterials, have garnered significant attention in the field of biomedical research in recent years. These quantum dots emit strong, stable light signals, exhibit exceptional optical properties, and can effectively bind to other molecules or biological components, making them ideal for use as labels [21, 22]. These advantages render quantum dots highly promising for pathogen detection, particularly in enhancing both the sensitivity and specificity of such assays [23]. In this study, we propose a novel PCR–QDFA detection platform. Following PCR amplification, the platform regulates the corresponding driving modules via distinct control commands to execute processes including washing, hybridization, incubation, and color development. The hybridization process is performed on a microarray chip, where reagents are added automatically—thereby reducing the contamination risk associated with manual hybridization procedures. Clinical samples were collected to assess the detection performance of the PCR‐QDFA platform. The results demonstrated that the PCR‐QDFA platform achieved high diagnostic accuracy, successfully detecting 95.21% of the target pathogens. The method also exhibited high overall concordance (86.73%), sensitivity (86.98%), and specificity (86.40%). Microbial DNA at extremely low concentrations can be detected by PCR technology, giving this method an advantage in identifying early‐stage infections. However, it is also more susceptible to environmental factors (such as sampling and sample transport) and residual dead microbial material, which can lead to false positives [24]. While traditional culture methods offer more reliable detection of live pathogens and effectively reduce the impact of contamination interference, their relatively high detection limits may lead to the omission of actual infections, resulting in false negatives [15].

Therefore, establishing reasonable thresholds and selecting appropriate detection methods are crucial safeguards for accurately distinguishing genuine infections from contamination in practical applications. First, a reasonable positive cutoff value must be set. When establishing this cutoff, we initially established a critical threshold based on negative control samples and statistical analysis. This helps define background signals and avoid false positives, potentially caused by contamination. We also incorporated clinically authentic samples. By comparing PCR‐QDFA results with those from culture methods—which exhibit greater specificity for living organisms—we further refined the threshold for genuine infections and maximized the reduction of false‐positive risks. These findings indicate that PCR‐QDFA is an effective and reliable technique for detecting UTI pathogens in clinical practice. Moreover, compared to traditional urine culture, this method offers several advantages, including a shorter turnaround time and improved sensitivity, making it a promising tool for the rapid and accurate diagnosis of UTIs.

PCR–QDFA demonstrated 100% sensitivity for several common pathogens, including Klebsiella pneumoniae, Citrobacter freundii, Enterococcus faecium, Pseudomonas aeruginosa, Enterobacter cloacae , Candida albicans , and Proteus mirabilis . This indicates that PCR–QDFA is nearly as effective as urine culture in detecting these pathogens, confirming its high efficiency and accuracy, especially for those more commonly found in urine samples. Klebsiella pneumoniae, Enterococcus faecium , and Pseudomonas aeruginosa are well‐known causes of hospital‐acquired UTI‐related bacteremia in children [25]. These bacteria are not only commonly found in hospital environments but also pose a significant challenge to clinical treatment due to their high levels of antibiotic resistance and invasiveness [26, 27, 28]. Studies show that the invasive nature of Klebsiella pneumoniae makes it a particularly dangerous pathogen, often causing UTIs, pneumonia, liver abscesses, and other infections, while also exhibiting significant antibiotic resistance [29, 30]. Similarly, the antibiotic resistance of Enterococcus faecium can significantly exacerbate the severity of infections and complicate the treatment process [31]. Pseudomonas aeruginosa is a globally recognized antibiotic‐resistant pathogen, and the World Health Organization has listed it as one of the drug‐resistant bacteria that requiring high priority attention [32]. Proteus mirabilis is known to form crystalline biofilms on both the surface and inside the lumen of urinary catheters, making it one of the most common causes of catheter‐associated urinary tract infections. It typically leads to conditions such as cystitis, pyelonephritis, and the formation of urinary stones [33]. PCR–QDFA not only enables rapid and accurate identification of these pathogens but also provides timely pathogen information to clinicians, facilitating swift decision‐making and reducing the risk of misdiagnosis and missed diagnoses.

However, the sensitivities for Enterococcus faecalis and Escherichia coli were 95.16% and 95.12%, respectively, which are slightly lower than those for other pathogens. This may be due to issues with sample collection, which could affect the quality of the samples and, in turn, PCR–QDFA's sensitivity. Additionally, the concentrations of these two bacteria may have been below the detection threshold of PCR–QDFA, resulting in them being undetectable. In the future, optimizing the detection threshold and improving sample collection procedures could further enhance the sensitivity of PCR–QDFA, particularly for low‐concentration pathogens. While traditional culture methods are regarded as the gold standard, they present particular challenges and are time‐consuming when it comes to detecting cell‐wall‐deficient pathogens such as Chlamydia trachomatis and Ureaplasma urealyticum , which require special conditions for growth. The culture process for these pathogens can be particularly difficult and time‐consuming, often leading to delays in obtaining accurate diagnostic information. Typically, from urine collection to pathogen identification, it takes 18 to 30 h [34, 35]. This diagnostic delay can result in worsened infection outcomes. Overall, PCR–QDFA demonstrates significant potential for rapid diagnosis with precision of UTIs, particularly in detecting and diagnosing multiple pathogens. This method could greatly enhance clinical diagnostic efficiency and play a crucial role in the timely identification of infections, ultimately leading to more effective patient management.

PCR–QDFA also has some limitations. First, the limited number of clinical samples restricts the ability to obtain more comprehensive clinical testing data. Second, the target range may not be sufficiently broad, potentially missing some pathogens and, as a result, failing to detect all infectious agents. In the future, we aim to enhance the application and development of PCR–QDFA in UTI diagnosis through ongoing technological innovation and method optimization.

5. Conclusion

In this study, we combined PCR technology with quantum dot fluorescence to develop a highly sensitive, highly specific, rapid, and automated PCR–QDFA detection platform capable of identifying 19 UTI pathogens. The platform was used to analyze 294 clinical urine samples, and the results demonstrated clear improvement. Compared to traditional culture methods, the PCR–QDFA platform significantly reduced diagnostic time while maintaining high sensitivity and specificity. It was proven to be an effective tool for diagnosing suspected UTI patients with clear clinical symptoms. In conclusion, this detection platform offers significant clinical value for the early diagnosis and treatment of UTIs, with promising potential for widespread clinical application.

Author Contributions

conceptualization: Huali Yin, Fei Zhao. data curation: Chunling Li. formal analysis: Qianwen Ye, Qian Meng. validation: Chunling Li, Pengfei Yu. writing – original draft: Chunling Li writing‐review and editing: Chunling Li, Chuanqing Wang.

Funding

The authors have nothing to report.

Ethics Statement

Approval was obtained from the Institutional Review Board of Children's Hospital of Fudan University.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Primer sequences and probe sequences used in the study

JCLA-40-e70177-s001.docx^{(15.4KB, docx)}

Table S2: Preparation ratio of solution A for each optimization plan

Table S3: Detection results of various pathogens under different ion concentrations of Solution A

Table S4: Preparation ratio of solution B for each optimization plan

Table S5: Detection results of various pathogens at different ion concentrations of Solution B

Table S6: Detection results of various pathogens at different hybridization times

Table S7: Detection results of various pathogens at different washing times

Table S8: Detection results of various pathogens at different hybridization and washing temperatures

Table S9: Detection results of various pathogens at different concentrations of incubation solution

Table S10: Detection results of various pathogens at different incubation times

Table S11: Detection results of various pathogens at different washing times after incubation

JCLA-40-e70177-s002.docx^{(8.3MB, docx)}

Data Availability Statement

The data that supports the findings of this study are available in the Supporting Information of this article.

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