Nano-Enabled Microfluidic Platforms for Functional Immunomonitoring in Pediatric Sepsis

Abstract

Pediatric sepsis involves dynamic, developmentally conditioned immune dysfunction that a single cytokine concentration or one-time severity label cannot represent. This structured narrative Review examines how nano-enabled microfluidic systems could support blood-sparing, serial assessment of functional immune states in the pediatric intensive care unit (PICU). We separate soluble-protein concentration, cell phenotype, ex vivo stimulation response, cellular effector function, immunometabolic function, and physiology, linking each measurement class to its evidence level and permitted inference. Direct pediatric studies support the biological relevance of longitudinal antigen-presentation, inducible cytokine, lymphocyte, and metabolic readouts, but cohorts remain few, small, and heterogeneous; secondary-infection findings are conflicting. Recent pediatric studies of serial soluble biomarkers and temperature trajectories expand monitoring evidence but do not validate functional immune trajectories. Engineering studies demonstrate nanoscale capture, amplification, transduction, and low-volume processing, including portable multicytokine sensing, yet these Level 4 results do not constitute pediatric clinical validity. We propose a staged sample-to-answer architecture. It distinguishes patient draw from device input, controls stimulation and preanalytics, retains developmental context, and progresses from analytical validation through pediatric feasibility and clinical validity to prospective prediction and decision utility. Artificial intelligence is restricted to age adjustment, longitudinal modeling, multimodal fusion, uncertainty display, and clinician- or nurse-facing visualization. Within the verified accessible corpus through 16 September 2026, no prospectively validated pediatric longitudinal immune-function trajectory model or nano-enabled functional immune assay for immunomodulatory treatment selection was identified. The near-term objective is a verifiable measurement-and-interpretation pathway, not an autonomous treatment selector, tested in multicenter serial cohorts with transparent blood-volume accounting, independent validation, and PICU human-factors evaluation.

Plain Language Summary

When a child develops sepsis, immune cells may become overactive, under-responsive, or change from one state to another during intensive care. Clinicians cannot see these changes from one cytokine concentration or one severity score. This Review explains how microfluidic devices that use nanoscale materials or sensors might test immune function repeatedly while using small analytical samples. Such devices could split blood into stimulated and unstimulated pathways, capture cells or molecules, amplify weak signals, and report changes over time. Yet a cartridge that uses little blood may still require a larger collection, a repeat draw, or a reference test. Sensitive detection also does not show that a test predicts outcomes or improves treatment decisions. Children need age-aware interpretation because immune responses change across development. We therefore propose a stepwise evaluation pathway: verify the measurement, demonstrate pediatric feasibility and patient-level blood savings, establish clinical validity, and only then test prediction and decision utility. Computer-assisted analysis may organize serial results, show missing data and uncertainty, and support clinicians and nurses, but it should not choose treatment independently. The immediate goal is a transparent, reliable monitoring pathway that families and PICU teams can evaluate prospectively.

Introduction

Pediatric sepsis may shift immune function over hours or days, while development alters the reference state used to interpret those changes. Direct pediatric studies have measured inducible cytokine release, antigen-presentation phenotype, lymphocyte responsiveness, and mitochondrial or immunometabolic function. These domains are not interchangeable. Their associations with organ dysfunction, treatment exposure, pathogen group, or immunoparalysis do not establish treatment selection or bedside utility.^1–3 The clinical problem is therefore specific: repeated, low-burden measurement of a child’s functional immune state with age, timing, treatment, and assay quality visible.

Nano-enabled microfluidic platforms may address parts of this problem by combining small-volume handling with nanoscale capture, enrichment, amplification, transduction, or antifouling. Existing studies demonstrate discrete modules, including nanoplasmonic cytokine sensing, phase nano-optical detection, nanobead-enhanced immunoassay, and microfluidic imaging of stimulus-dependent secretion.^4–7 They do not demonstrate an integrated functional assay validated in pediatric sepsis or a deployable PICU system.

This Review separates four evidence bodies: direct pediatric immune biology; blood-sparing and workflow evidence; Level 4 nano/microfluidic modules; and adult or related-pediatric bridge evidence. It also separates six measurement classes and six translational claim stages. Within the verified corpus, no pediatric study validated a nano-enabled microfluidic functional assay for immunomodulatory treatment selection or bedside decision utility. The integrated architecture is therefore an author-developed synthesis, not an observed platform class.

Our objective is to match each pediatric biological question to a demonstrated nanoscale function and a realistic PICU workflow. We examine acquisition, controlled stimulation, cellular adequacy, paired readout, developmental calibration, longitudinal analysis, quality control, and human review. This structure gives clinicians, engineers, data scientists, nurses, and implementation teams a common pathway from analytical performance to decision utility (Figure 1 and Table 1).

Figure 1.

Measurement taxonomy and inference firewall. (A) Six classes are distinguished: soluble-protein concentration, cell phenotype, ex vivo stimulation response, cellular effector function, immunometabolic function, and adjunct physiology. Pediatric functional, soluble-biomarker, adult LPS-stimulation, and nano-cytokine studies show why these classes are not interchangeable.^1–3,8–17 (B) Levels 1–4 denote author-defined translational proximity, not universal study quality; maturity and certainty are separate dimensions. (C) Analytical validation, clinical association, prediction, decision utility, and implementation are separated by inference firewalls, each requiring a new study design. In panel A, the symbol ≠ means “is not equivalent to”: soluble-analyte concentration does not directly measure cellular functional response.

Table 1.

Measurement Classes and Permitted Inferences

Measurement Class	Specimen/Perturbation	Functional-Chain Classification	Readout and Biological Meaning	Minimum Controls	Permitted Inference	Prohibited Inference	Translational-Proximity Evidence Lane	Within-Lane Maturity/Certainty	References
Plasma protein concentration	Plasma or serum; no perturbation	Soluble-analyte sensing	Soluble mediator concentration at the sampling time; not a cellular functional response	Matrix, calibration, hemolysis, timing and treatment context	Concentration of the measured mediator at the stated time	Cellular response capacity, global immune competence, prediction or treatment utility	Levels 1–4 may contribute, depending on population and platform	Source-specific and heterogeneous; analytical or associative maturity does not imply clinical utility	[1–7]
Cell phenotype	Fresh whole blood or isolated cells; no perturbation	Partial functional module (phenotyping only)	Surface or intracellular marker abundance; identity, activation or antigen-presentation phenotype	Gating, viability, absolute counts, compensation and batch controls	Phenotypic state under the measured conditions	Stimulus responsiveness, effector function, pediatric decision threshold or treatment benefit	Primarily Levels 1–2 in this review	Cohort-bounded association evidence; threshold transportability and decision utility remain unestablished	[1,18–20]
Ex vivo stimulation response	Fresh whole blood or isolated cells; defined agonist, dose and incubation	Complete functional assay only when perturbation, response, controls, normalization and QC are all documented	Stimulus-induced cytokine release or marker change; perturbation-specific response capacity	Matched unstimulated control, agonist lot/dose, delay, temperature, incubation and cell composition	Response to the defined perturbation under the stated assay conditions	Global immunity, cross-assay equivalence, pediatric threshold, prediction or decision utility	Levels 1–2 for clinical biology; Level 4 for enabling modules	Direct pediatric studies remain concentrated; assay standardization and independent replication are incomplete	[1,2,21–25]
Cellular effector function	Whole blood or isolated cells; assay-specific challenge	Complete functional assay when challenge-to-output chain and controls are preserved	Proliferation, cytotoxicity, phagocytosis or microbial killing; assay-specific effector reserve	Viability, target-to-effector ratio, recovery and matched negative/positive controls	Assay-specific cellular function under the tested conditions	Clinical benefit or competence of the entire immune system	Levels 1–3 in this review	Domain- and assay-specific maturity; pediatric replication and utility are limited	[2,26]
Immunometabolic function	PBMCs or defined subsets; basal and/or stimulated conditions	Partial or complete functional module depending on perturbation, composition control and normalization	Respiration, bioenergetics or metabolite profiles; metabolic state or reserve	Cell composition, viability, normalization, handling time and treatment exposure	Association with the measured immunometabolic state	Intrinsic cell defect without cell-specific control, prediction or treatment utility	Level 1 for current pediatric biology	Repeated observations exist within linked cohorts; composition confounding and external replication constrain certainty	[1–3,27]
Clinical physiology	Bedside monitor or wearable-derived signal; no immune perturbation	Adjunctive context—not a functional immune assay	Vital-sign, activity or physiological trajectory that may contextualize immune measurements	Signal quality, missingness, device identity, synchronization and artifact handling	Adjunct physiological context	Cellular immune function, replacement of functional assays or autonomous treatment guidance	Level 3/context-only implementation bridge	Transfer-principle maturity only; assay-specific pediatric implementation is unvalidated	[28–30]

Notes: Level 1–4 are translational-proximity evidence lanes, not a universal quality hierarchy: Level 1, direct pediatric sepsis clinical evidence; Level 2, adult sepsis clinical bridge evidence; Level 3, related pediatric or critical-illness evidence; Level 4, engineering, analytical or proof-of-concept evidence. Maturity/certainty is appraised independently within each lane. Measurement classes are not interchangeable. “Author-proposed integration” refers to a synthesis architecture assembled from separately demonstrated components and is not a validated integrated system.

Abbreviations: PBMC, peripheral-blood mononuclear cell; QC, quality control.

The review’s originality lies neither in cataloging devices nor in treating every immune abnormality as one endotype. It lies in aligning a defined pediatric functional question with a demonstrated nanoscale operation, then exposing the missing validation steps. Direct pediatric studies define biological targets and confounders; engineering studies define candidate mechanisms. Their separation is a limitation of the field and the reason that integration, workflow, and decision claims must be tested sequentially rather than inferred.^4,5

Literature Search and Evidence Synthesis

This structured narrative Review used predefined searches across PubMed/MEDLINE, Crossref, OpenAlex, Semantic Scholar, Scopus, Web of Science, IEEE Xplore, and the journal archive. The principal window, 30 July 2018 to 30 July 2026, captured contemporary pediatric sepsis immunobiology and nano-enabled microfluidics. Earlier seminal or methodological studies remained eligible through anchor searches and backward citation chaining. A reviewer-driven post-cutoff update covered 31 July to 16 September 2026. A separate reviewer-triggered backfill searched 1 January to 30 July 2026 for prespecified automated-cartridge, controlled functional-assay, and single-cell or advanced-device classes that may have been missed by the original platform search. The update and backfill targeted pediatric functional immune trajectories, nano-enabled devices, PICU workflow, decision utility, longitudinal AI, and artifact-specific citations. Search strings, dates, sources, and dispositions are reported in Supplementary Files S1, S2, and S3A–S3D.

We classified evidence as direct pediatric sepsis (Level 1), adult sepsis bridge (Level 2), related pediatric or critical-illness (Level 3), or engineering/analytical proof-of-concept (Level 4). Levels indicate translational proximity, not study quality. The submitted search screened 5948 records at title/abstract and 1180 at full text, retaining 171 full texts. The update added 12 bounded sources, producing 183 accessible full texts: 6 core, 130 bridge, and 47 context-only. A core pediatric record required direct non-neonatal pediatric sepsis data, an eligible functional, phenotypic, or immunometabolic immune measure, extractable methods and outcome or claim anchors, and verifiable full text. Linked cohort or version families were counted once for independence and used only for non-duplicative questions. One predefined workflow was used without independent duplicate screening or extraction.

Limitations of the Evidence Synthesis and Current Field

This approach limits certainty. A single predefined workflow reduced, but did not remove, selection and extraction bias. Records without verified full text were excluded from support, contradiction, and global absence claims; database indexing and access constraints may leave eligible work unidentified. Evidence classification and cohort-family linkage required author judgment. Direct pediatric functional evidence remains concentrated in few, often small or related cohorts, limiting precision, external validity, thresholds, and subgroup inference. Heterogeneous populations, assays, sampling schedules, and outcomes precluded a defensible pooled effect estimate. Adult and engineering evidence can define mechanisms and validation requirements but cannot establish pediatric clinical validity.

Current Understanding of Functional Immunomonitoring

Functional immunomonitoring requires explicit measurement-class labels. We distinguish soluble-protein concentration, cell phenotype, ex vivo stimulation response, cellular effector function, immunometabolic function, and physiological signal.⁸ A cytokine concentration describes soluble abundance at one time; it does not show how much a leukocyte population can produce after a standardized challenge. Recent pediatric studies of serial interleukin-6 and heparin-binding protein broaden soluble-biomarker evidence, but they do not measure inducible cellular capacity.^14,15

Cell phenotype is also distinct from function. Monocyte human leukocyte antigen-DR (mHLA-DR) is a surface-expression phenotype associated with antigen presentation, whereas stimulated tumor necrosis factor alpha (TNF-α) release measures a response to perturbation.^9,10 Pediatric work shows that mitochondrial function, mHLA-DR, and stimulated cytokine responses can have different associations within one clinical setting.¹ Transcriptomic differences between response-defined groups provide mechanistic context, not an interchangeable bedside assay.¹⁹

Cellular effector and immunometabolic measurements require equally precise labels. Lymphocyte proliferation or stimulus response interrogates cellular reserve, while mitochondrial respiration interrogates energetic function.^11–13 Exploratory pediatric serial studies linked lymphocyte-response abnormalities with immunometabolic features and pathogen group.² Mixed-cell measurements also depend on cell composition, so concurrent counts and normalization are necessary.³

Physiological signals, including heart rate, oxygenation, temperature, and blood pressure, form a separate contextual layer. They can improve temporal interpretation, but they are not cellular immune-function assays. Any multimodal model must preserve the provenance, timing, missingness, and uncertainty of each input. High-frequency physiology must not obscure sparse but biologically specific immune measurements.

The Level 1–4 classification describes translational proximity rather than study quality. Within each level, certainty depends on design, population and matrix fit, source anchoring, cohort independence, replication, prespecification, and uncertainty. The same boundary governs verbs: analytical studies detect, clinical studies may associate, validated models predict, and prospective action pathways may guide. No downstream claim follows automatically from an upstream result.

Interpretation also depends on the biological denominator. Stimulated release per blood volume, per cell count, or relative to an unstimulated control answers different questions. Surface-marker intensity depends on gating and calibration, while nano-optical output depends on surface chemistry and reference correction.^1,5,18 Harmonization therefore requires the stimulus, cells, controls, timing, matrix, and normalization, not merely the same analyte name (Figure 1 and Table 1).

Every platform description should therefore state its measurand, matrix, stimulus, incubation, cell-adequacy rule, denominator, control path, and intended claim. Terms such as immune suppression, recovery, trajectory, and blood-sparing are otherwise ambiguous. A phenotype may be biologically coherent yet disagree with a stimulation response, and a metabolic abnormality may coexist with preserved output elsewhere. Such discordance should prompt examination of timing, cell redistribution, treatment, and assay process rather than collapse every abnormal measurement into one immune state.

Analytical sensitivity should also be matched to the biological contrast. An extreme detection limit may add little if the relevant response spans a higher range or if preanalytical variability dominates. Conversely, a less sensitive assay may be clinically informative when it preserves perturbation, controls, and cellular normalization. The appropriate benchmark is therefore claim-specific reproducibility and information content, not sensitivity alone. This distinction guides the platform comparisons and the staged validation roadmap below.

Domains may eventually be combined, but only after each component has a stable definition. A multimodal score that mixes phenotype, stimulated output, metabolism, and physiology can change because one channel fails, because cell composition shifts, or because the child’s biology changes. Domain-specific outputs and visible quality flags preserve interpretability. Combination should therefore occur at an explicitly validated analytical layer, not through unlabeled narrative or numerical aggregation.

Pediatric Immune Biology and Developmental Calibration

Developmental calibration is a biological requirement, not a statistical refinement added after assay construction. Direct pediatric evidence associates age with selected memory and regulatory T-cell profiles.^31,32 This supports age-aware interpretation, but it does not define universal pediatric cut-offs or treatment thresholds. Initial studies should retain exact age, prespecify defensible strata, and report uncertainty where age-specific data are sparse.

The best-developed direct pediatric functional domain is inducible innate response. Studies used ex vivo stimulation and stimulated cytokine release to define immunoparalysis, with mHLA-DR as a related but non-identical phenotype.¹⁹ In 102 children with severe sepsis or septic shock, a 50-µL whole-blood stimulation aliquot was used. Hydrocortisone exposure was associated with longer multiple-organ-dysfunction duration only within an immunoparalysis subgroup, but this observational interaction did not validate treatment selection.¹⁸

Transcriptomic profiling provides a biological bridge rather than a ready diagnostic. A proof-of-principle analysis compared 32 children selected from opposite ends of a stimulated-response classification within a larger parent cohort.¹⁹ Exclusion of intermediate responses and vulnerability to baseline and cell-count differences limit generalization across ages, centers, pathogens, and serial time points.

Lymphocyte and immunometabolic measurements extend the model beyond monocytes. One exploratory study included 14 children with sepsis and seven PICU controls across 34 serial samples; responses and metabolic abnormalities varied by pathogen category.² A larger dataset contained 151 sepsis patients and 286 blood-count-paired time points, showing that mixed-cell mitochondrial measurements were influenced by cell composition.³ These findings support domain-specific outputs, not a single omnibus immune score.

Microbiome and metabolite observations add context but not causal resolution. A Children’s Hospital of Philadelphia (CHOP) family report linked intestinal diversity and short-chain-fatty-acid patterns with immunometabolic measures.²⁷ It addresses a distinct question but does not add an independent cohort unit. Nutrition, antimicrobial exposure, sampling time, and other covariates must remain visible when interpreting these associations.

Secondary infection remains a candidate endpoint rather than defining proof of immunoparalysis. Across pediatric cohorts, findings varied with assay, timing, treatment, and cohort construction; null and discordant results were retained.^1,2,18,19 Early immune measurements may also reflect severity and exposure opportunity rather than a causal susceptibility state.

Longitudinal interpretation should prioritize within-child change without assuming an age-neutral baseline. Adult mHLA-DR and functional-profile studies support temporal variation, but their thresholds and schedules cannot be imported into pediatrics.^20,21 A pediatric model should encode age, sepsis timing, treatments, pathogen context, cell composition, and measurement quality while preserving domain-specific discordance.

Fisler et al enrolled participants younger than 21 years, but the official report and accessible supplement provide no verifiable 1-month–18-year subset or individual-level age restriction. We therefore retain the study as scope-limited related pediatric evidence for developmental context, not as Level 1 core evidence. The six in-scope core publications resolve to two confirmed independent cohort units, with one additional Nationwide family remaining overlap-uncertain; the Fisler Northwell cohort is reported separately and is not counted in either total. Four CHOP reports count once, and linked reports answer only non-duplicative questions. This concentration limits precision, external validity, threshold development, and subgroup inference. Evidence should therefore be described as assay-specific biological plausibility rather than replicated clinical utility (Figure 2 and Table 2).³¹

Figure 2.

Developmentally calibrated longitudinal immune-state framework. (A) Immune reference context changes across childhood, so interpretation requires age-aware calibration rather than a single pediatric threshold.^31,32 (B) Antigen presentation, stimulated response, lymphocyte reserve, neutrophil function, and immunometabolic function are separate domains; physiology provides context only.^{1–3,8–13,19} (C) Curves represent conceptual within-patient trajectories, not patient-derived values or validated thresholds. Adult longitudinal evidence supports temporal variation, whereas direct pediatric functional series remain limited.^{2,20,21,33,34} (D) Organ dysfunction, recovery, and secondary infection are candidate endpoints with assay- and time-specific associations; secondary-infection evidence is conflicting.^18,23,35,36 Pediatric temperature trajectories provide physiological context, not functional immune trajectories.³⁷

Table 2.

Direct Pediatric Functional Evidence and Bounded Adult Bridges

Publication/Lane	Parent Cohort	Site; Enrollment	Population/Age; Sample Size	Assay Subset	Cohort Family	Overlap Status	Independent Replication Status	Bounded Contribution	Generalizability Limitation	References
Weiss et al, 2020/Level 1	CHOP prospective pediatric sepsis mitochondrial/immune-function study	CHOP; May 2014–June 2018	<18 years; <7.5 kg excluded; 204 enrolled, 161 sepsis analyzed, 18 controls	Parent serial PBMC respiration, mHLA-DR and LPS-stimulated TNF; assay denominators vary	L1CF-CHOP-MITOIMMUNE-2014–2018	CONFIRMED shared parent family with refs2,3,27	CONFIRMED_INDEPENDENT as family representative; count CHOP family once	Pediatric association and serial assay-domain evidence; no decision utility	Single center; low-weight children excluded; later sampling incomplete; linked publications do not provide external replication	[1]
Lindell et al, 2022/Level 1	Same CHOP 2014–2018 parent study	CHOP; May 2014–June 2018	<18 years; <7.5 kg excluded; 14 sepsis + 7 controls; 34 serial sepsis samples	Planned lymphocyte/immunometabolic secondary subset; journal/preprint version family	L1CF-CHOP-MITOIMMUNE-2014–2018	LINKED_OR_NESTED	Not independently countable	Assay-specific lymphocyte and immunometabolic detail; within-family convergence only	Small, adolescent-skewed subset; no treatment-selection validation	[2]
Weiss et al, 2022/Level 1	Same CHOP 2014–2018 parent study	CHOP; May 2014–June 2018	<18 years; <7.5 kg excluded; 167 with mitochondrial measures; 151 contributed 286 CBC-paired observations	Secondary PBMC-composition reanalysis; detailed subsets in heavier consenting participants	L1CF-CHOP-MITOIMMUNE-2014–2018	SAME_COHORT_DIFFERENT_ANALYSIS	Not independently countable	Cell-composition confounding and interpretation of PBMC respirometry	Single center; mixed-cell confounding; purified-cell validation limited by blood availability	[3]
Bline et al, 2020/Level 1	Previously published Nationwide prospective severe-sepsis/septic-shock immune-phenotyping cohort	Nationwide Children’s; Jan 2012–Apr 2014	<18 years; median 75 months; 102 participants	Hydrocortisone/MODS secondary analysis stratified by early LPS-stimulated TNF response	L1CF-NCH-SEVERE-SEPSIS-2012–2014	LINKED_OR_NESTED within its stated parent cohort	One underlying Nationwide family unit; publication itself not independent	Association between hydrocortisone exposure and outcome within an immunoparalysis subgroup; no causal treatment inference	Single center; nonprotocolized exposure, residual confounding and no treatment-selection validation	[18]
Snyder et al, 2021/Level 1	Nationwide prospective septic-shock parent study of 68 enrollees	Nationwide Children’s; enrollment period not reported in current verified extract	Pediatric septic shock; exact age range not reported; 32 selected extremes from 68 enrollees	Selected LPS-TNF response extremes with whole-blood RNA sequencing; intermediate/transient phenotypes excluded	L1CF-NCH-TRANSCRIPTOMIC-PARENT68-UNDATED (provisional)	OVERLAP_UNCERTAIN relative to ref18	Not independently countable under conservative rule	Transcriptomic association in selected response groups; no independent threshold validation	Parent protocol/timing unavailable; selected extremes and baseline/cell-count differences limit transportability	[19]
Fisler et al, 2025/Level 3 scope-limited pediatric bridge	Standalone Northwell prospective observational cohort, IRB 20–0191-CCMC	Cohen Children’s/Northwell; Jul 2020–Sep 2022	<21 years; 80 across sepsis and comparator groups; no extractable 1-month–18-year subset	Day-1 immune profiling and stimulation; days 3 and 7 planned but later samples insufficient	L1CF-NORTHWELL-IMMUNE-PROFILE-2020–2022	NO_VERIFIABLE_1-MONTH–18-YEAR_SUBSET	Not countable within locked 1-month–18-year core scope	Developmental-heterogeneity bridge only; excluded from core publication and independent-unit counts	Single center; age definition exceeds locked scope; limited serial completeness	[31]
Weiss et al, 2021/Level 1	Pilot performed in conjunction with the CHOP mitochondrial-dysfunction parent study	CHOP; Jan 2017–Jun 2018	3 to <18 years; 43 sepsis analyzed; blood 41, stool 32; healthy stool controls 44	Microbiome/SCFA pilot with PBMC respiration and LPS-stimulated TNF	L1CF-CHOP-MITOIMMUNE-2014–2018	LINKED_OR_NESTED	Not independently countable	Noncausal microbiome/immunometabolic context; not independent functional validation	Children <3 years excluded; incomplete serial sampling and diet/antibiotic/transit confounding	[27]
Monneret et al, 2025/Level 2 adult bridge	Lyon adult septic-shock real-world cohort	Adult ICU; 20-year cohort	Adults with septic shock; 1023 cases	Serial mHLA-DR	Adult Lyon cohort family	Linked analyses counted once	Not pediatric replication	Adult biological and workflow plausibility for serial phenotype measurement	Adult thresholds, prediction and treatment guidance cannot be transported to children; corrected Figure 2G/H governs figure interpretation	[20]
Halstead et al, 2025/Level 2 adult bridge	Penn critical-illness cohort	Adult critical illness; secondary analysis	Adults; 18 participants	Ex vivo stimulation, stimulated cytokine release, lymphocyte-related and soluble profiling	Adult Penn cohort family	Linked to Samuelsen et al	Not pediatric replication; not independent of ref22	Exploratory functional-profile differences across adult inflammatory subphenotypes	Small secondary analysis; no pediatric threshold, prediction or treatment-guidance inference	[21,22]
Samuelsen et al, 2024/Level 2 adult bridge	Penn critical-illness cohort	Adult enrollment within 48 h; days 1, 7 and 14	Adults >18 years; 31 sepsis + 33 nonsepsis controls	mHLA-DR, lymphocyte count, LPS-stimulated TNF/IL-6 and stimulated IFN-γ	Adult Penn cohort family	Linked to Halstead et al	Not pediatric replication; same family as ref21	Adult longitudinal bridge for temporal assay behavior	Observational heterogeneous cohort; temporal change does not establish recovery, prognosis or treatment utility	[21,22]

Notes: Six in-scope Level 1 publications resolve to two confirmed independent cohort units, with one additional Nationwide family overlap-uncertain. Fisler et al is retained as scope-limited related pediatric evidence because the <21-year cohort has no verifiable 1-month–18-year subset and is not included in the core or independent-unit counts. Four CHOP reports count once; linked reports answer only non-duplicative questions. Adult Level 2 rows are bridges only and cannot establish pediatric thresholds, prediction, treatment guidance, or decision utility.

Age calibration should not default to broad bins when response capacity changes continuously across childhood. However, small cohorts cannot support highly flexible curves without instability. A staged design can prespecify clinically defensible strata for feasibility, retain exact age for modeling, and use shrinkage or uncertainty at sparsely sampled ages. Pubertal status, prior immune disease, nutrition, chronic technology dependence, and developmental context may matter, but they require prospective collection rather than post hoc inference.

Perturbation design creates additional biological specificity. A weak induced response may reflect cellular exhaustion, low cell abundance, medication exposure, inadequate stimulation, delayed processing, or technical loss. Interpretable assays therefore require an unstimulated control, a positive technical or biological control where feasible, and a cell-adequacy measure. These controls are especially important in pediatric critical illness, where leukocyte counts and available sample volume vary and where an invalid result can trigger another blood draw.

Developmental and clinical heterogeneity must be separated analytically. Age may alter the expected reference value, while organ dysfunction, pathogen, treatment, and sampling time may alter the sepsis trajectory.^2,18,31 Adjustment for age alone can still confound treatment exposure with recovery; adjustment for severity alone can erase genuine developmental variation. The direct pediatric evidence therefore supports a minimum covariate set, but not a universal correction equation.

Neonatal evidence should remain a developmental comparator rather than be pooled with the non-neonatal PICU population. Immune maturation, disease definitions, sampling constraints, pathogen distributions, and care pathways differ substantially in newborns. A neonatal marker or threshold may generate a hypothesis for older children, but transport requires direct validation. The same caution applies when related pediatric inflammatory or respiratory cohorts supply engineering or workflow evidence without sepsis-specific biological validation.

Blood-Sparing Longitudinal Assessment

Low-volume and blood-sparing are not synonyms. Analytical device input is the volume entering the assay; patient blood draw also includes line-clearance or discard volume, dead space, failed runs, repeats, and reference procedures. Small device input can therefore coexist with substantial burden. Both denominators and cumulative burden across the monitoring episode should be reported.³⁸

A functional assay must control anticoagulant, time to stimulation, stimulant identity and dose, incubation, temperature, mixing, cell viability, and normalization.^18,19 A recent 100-µL label-free microfluidic workflow recovered cells within five minutes while preserving platelet quiescence and neutrophil–platelet aggregates. It is a blood-processing bridge, not a pediatric sepsis assay.³⁹ Adult point-of-care lipopolysaccharide (LPS)-stimulated TNF-α testing further shows that the tube, processing interval, controls, operator, and validity rules must be explicit.¹⁶

Longitudinal studies should harmonize collection windows, record treatments before each draw, retain actual intervals, and document missed samples. Mixed-cell metabolic measurements require concurrent composition data because changing cell proportions can mimic intrinsic change.^2,3 Missingness may reflect instability, vascular access, anemia risk, or workload and should not be hidden by interpolation.

The minimum interpretable dataset includes age, time from sepsis recognition, assay lot and quality-control status, relevant therapies, cell counts, and prior patient-specific values. Adult trajectories can inform modeling, but not pediatric thresholds.^20,33 The near-term pediatric goal is a reproducible description of direction, magnitude, and uncertainty against a developmentally appropriate comparator.³¹

A reporting ledger should separate directly supported measures, clinically or technically derived requirements, and author-proposed criteria. It should record total blood obtained, device input, invalid and repeat-draw frequency, cell recovery and viability, matrix dilution, turnaround, hands-on time, and waste. Candidate patient-level endpoints include cumulative diagnostic loss, first-pass success, discomfort, staff time, and reference-assay draws. These are evaluation measures, not established benefits (Figures 2 and 3, Tables 3 and 4).

Figure 3.

Nano-enabled sample-to-answer functional assay architecture. (A) Patient blood draw and device input are distinct denominators; discard volume, reference tests, failed runs, and repeats belong in patient-level accounting.^{17,26,33,38–46} (B) The preanalytical chain records tube and anticoagulant, timing, matrix handling, adequacy, stimulation, incubation, controls, and deviations.^{1–3,16,18,19,24,25} (C) Magnetic nanobeads, nanostructured affinity surfaces, laser-induced graphene, and microfluidic enrichment provide alternative capture or processing modules.^5,39,46–49 (D) Nanoelectronic, plasmonic, surface-enhanced Raman scattering (SERS), nanozyme, and electrochemiluminescent routes provide alternative transduction or amplification modules.^{4–7,17,48–69} (E) Antifouling, internal controls, normalization, lot/operator tracking, invalid status, and visible uncertainty accompany any functional-chain label.^{16,17,24,25,46,70,71}

Table 3.

Selected Nano-Enabled and Functional-Assay Platform Exemplars

Source/Platform	Biological Input; Measurement Class	Perturbation/Incubation	Nanoscale Capture or Enrichment	Transduction/Amplification	Controls/Normalization	Functional-Chain Status	Demonstration Status	Evidence Lane; Maturity/Certainty	Pediatric Applicability Boundary	Nano Role; Conventional Comparator; Advantage; Failure Mode	References
Park et al, 2025/live single-cell nanoplasmonic array	Stimulated immune-cell model; spatial-temporal IL-6 secretion	Controlled model-system stimulation and timed observation	Aptamer-functionalized gold nanoplasmonic structures	Localized surface plasmon resonance imaging	Model-system controls; no pediatric normalization or bedside QC established	PARTIAL_FUNCTIONAL_MODULE	DEMONSTRATED_MODULE	Level 4; proof-of-concept model-system maturity	Not pediatric sepsis; secretion dynamics do not establish a validated pediatric functional assay or clinical utility	ESSENTIAL; conventional fluorescence/ELISA; live spatial-temporal secretion; fouling, drift, and aptamer-response variability	[4]
Wu et al, 2026/digital nanoplasmonic immunoassay	Cytokine standards and in vitro CAR-T model; soluble-analyte sensing	In vitro activation model; not a patient functional-assay chain	Peptide-aptamer capture and plasmonic nanoparticle counting	Digital optical counting/signal transduction	Analytical calibration in controlled matrices; no cellular normalization chain	SOLUBLE_ANALYTE_SENSING	DEMONSTRATED_MODULE	Level 4; analytical/in-vitro maturity	Microliter input is not patient-level blood sparing; no pediatric clinical validity	ESSENTIAL; conventional plate immunoassay; low-volume digital sensitivity; nonspecific binding, saturation, and calibration drift	[5]
Saateh et al, 2026/multiplex nanoplasmonic digital assay	Unprocessed serum; soluble-analyte sensing	None	One-step immunocapture	Nanoplasmonic digital counting with kinetic profiling	Analytical calibration and multiplex controls; no patient blood ledger	SOLUBLE_ANALYTE_SENSING	DEMONSTRATED_MODULE	Level 4; analytical validation in serum	<10 µL device input does not prove patient-level blood sparing, function, or clinical validity	ESSENTIAL; conventional serum immunoassay; multiplex digital counting and kinetics; matrix effects, capture variability, and particle-counting bias	[72]
Maw et al, 2026/label-free small-volume blood processor	100 µL whole blood; cell-preserving preanalytical processing	None; processing only	No explicit nanoscale function	Label-free microfluidic fractionation; no sensing transduction	Cell recovery and activation-state assessment	ORDINARY_MICROFLUIDIC_ PREANALYTICAL_BRIDGE	DEMONSTRATED_MODULE	Level 4; blood-processing proof of concept	Five-minute processing and cell recovery do not establish pediatric sepsis assay performance, functional readout, or patient-level blood sparing	ILLUSTRATIVE (NON-NANO); conventional centrifugation/FACS preparation; rapid small-volume cell-preserving processing; recovery bias, clogging, and activation	[39]
Cisteró et al, 2026/whole-blood LPS-induced TNF-α test	1 mL fresh lithium-heparin adult whole blood; induced TNF-α release	LPS stimulation for 2.5 hours	No explicit nanoscale function	Qualitative lateral-flow TNF-α readout	Assay-validity and multisite/operator/lot reproducibility; no pediatric calibration	PERTURBATION_BASED_ FUNCTIONAL_COMPARATOR	DEMONSTRATED_ADULT _CLINICAL_BRIDGE	Level 2 adult clinical bridge; non-nano/non-microfluidic	No pediatric cohort, nanoscale integration, longitudinal measurement, treatment selection, or decision utility	ILLUSTRATIVE (NON-NANO); centralized stimulation plus ELISA; point-of-care functional workflow comparator; lot, operator, timing, and control failure	[16]
Yu et al, 2026/portable nanozyme electrochemical multicytokine platform	Frozen adult plasma; soluble cytokine concentrations; approximately 30 µL for six analytes	None	Ti3C2Tx MXene/AuNP nanozyme immunocapture and amplification	Enzyme-cascade electrochemical readout with portable workstation	Matrix, batch, and stability testing; same-site 7:3 model holdout	SOLUBLE_ANALYTE_ NANOELECTROCHEMICAL_SENSING	DEMONSTRATED_ADULT _SAMPLE_MODULE	Level 4 with adult clinical samples; single-center	Not ex vivo cellular function, pediatric, longitudinal, microfluidically integrated, or independently externally validated	ESSENTIAL; conventional electrochemical/ELISA assay; catalytic amplification in a portable multiplex format; fouling, batch instability, and model overfit	[17]
Pan et al, 2026/paper microfluidic laser-induced-graphene IL-6 sensor	PBS, artificial serum, and human serum; soluble IL-6	None	Laser-induced-graphene sensing electrode in paper microfluidics	Label-free electrochemical impedance spectroscopy	Analytical selectivity; clinical cohort, input volume, and total time not reported in the accessible abstract	SOLUBLE_ANALYTE_SENSING	DEMONSTRATED_COMPONENT	Level 4; analytical component maturity	No reported pediatric sepsis, cellular-function, prognostic, or decision-utility validation	ESSENTIAL; conventional planar electrode/ELISA; label-free paper integration; matrix interference, electrode drift, and absent clinical cohort	[49]
Füredi et al, 2026/automated digital-microfluidic cytokine cartridge	8 µL diluted frozen plasma from related pediatric critical illness; soluble cytokines	None	Magnetic-bead immunocapture; no distinct nanoscale function established	Automated digital microfluidics with digital fluorescence readout	Intra-cartridge CV 6.25%; inter-cartridge CV 3–21%; paired n=20–21	AUTOMATED_MICROFLUIDIC _SOLUBLE_ANALYTE_BRIDGE	DEMONSTRATED_RELATED _PEDIATRIC_BRIDGE	Levels 3–4; related-pediatric/engineering bridge	Frozen diluted plasma; not pediatric sepsis, fresh whole blood, cellular function, or patient-level blood-sparing validation	ILLUSTRATIVE (NON-NANO); conventional plate immunoassay; automated small-volume multiplexing; cartridge-to-cartridge variation, dilution, and matrix effects	[46]
Zhao et al, 2027/porous-silicon-nanoparticle ECL immunosensor	Analytical IL-1β/IL-6 immunoassay; clinical cohort and input not reported in the accessible abstract	None	Mildly oxidized porous-silicon nanoparticles enrich Ru(bpy)32⁺ at the electrode	Electrochemiluminescence amplification	Analytical proof of concept; clinical matrix workflow not established	NANOELECTRODE_ TRANSDUCTION_COMPONENT	DEMONSTRATED_ COMPONENT	Level 4; 2026online-first/2027-assigned component	No reported clinical, microfluidic, functional, or pediatric validation	ESSENTIAL; unmodified electrochemiluminescent electrode; porous-silicon signal enrichment; particle consistency, oxidation, and matrix transfer	[69]
Liu et al, 2027/PEG-PDMS antifouling whole-blood EV workflow	Preprocessed whole blood; extracellular-vesicle quantification	None	PEG-modified PDMS reduces nonspecific adsorption; no immune-cell capture	Portable fluorescence-polarization immunoassay	Complex-matrix assessment; preprocessing remains required	ANTIFOULING_COMPLEX _MATRIX_COMPONENT	DEMONSTRATED _COMPONENT	Level 4; 2026 online-first/2027-assigned component	EV target and preprocessing are not cytokine function, pediatric sepsis, or PICU validation	OPTIONAL/ENABLING; unmodified PDMS; reduced nonspecific adsorption in complex matrix; coating degradation, preprocessing dependence, and target mismatch	[71]

Notes: Rows were selected to represent distinct enabling functions or functional comparators rather than a device catalogue. The platforms are alternative demonstrated modules; none integrates the complete functional chain in a validated pediatric sepsis cartridge. A complete functional assay requires a defined biological input, controlled perturbation where relevant, timed incubation, preserved response readout, appropriate controls, normalization, assay-validity criteria, and QC/failure status. “Author-proposed integration” is the review’s synthesis of separately demonstrated modules. Soluble-analyte concentration, extracellular-vesicle measurement, and ordinary microfluidic processing are not cellular functional measurements. Device input is not patient draw. The nano-role column uses ESSENTIAL when the nanoscale operation is necessary to the claimed signal or interface, OPTIONAL/ENABLING when it improves robustness but is not the biological measurand, and ILLUSTRATIVE when the row is a non-nano comparator or bridge. The comparator, stated advantage, and failure mode are operational design hypotheses, not demonstrated pediatric benefits. Level 4 denotes engineering/analytical proximity, not low study quality, and does not establish pediatric clinical validity, prediction, decision utility, or bedside maturity.

Abbreviations: AuNP, gold nanoparticle; CAR-T, chimeric antigen receptor T cell; CV, coefficient of variation; ECL, electrochemiluminescence; EV, extracellular vesicle; IL, interleukin; LPS, lipopolysaccharide; LSPR, localized surface plasmon resonance; MXene, transition-metal carbide/nitride; PDMS, polydimethylsiloxane; PICU, pediatric intensive care unit; QC, quality control; TNF, tumor necrosis factor.

Table 4.

Developmental, Preanalytical, and Blood-Sparing Reporting Requirements

Domain	Required Variable/Denominator	Why it Matters	Minimum Reporting Item	Candidate Patient-Level Evaluation Endpoint	Provenance	Priority	Current Gap/Proposed Validation	References
Development	Exact age and developmental stratum	Immune baselines and response capacity vary across development	Exact age, prespecified age band and comparator source	Age-stratified feasibility, validity and subgroup performance	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	No validated age-specific decision thresholds; use multicenter age-stratified reference and sepsis cohorts	[1–3,31]
Patient blood draw and cumulative burden	Total blood obtained, line-clearance/dead/discard volume, reference-assay draws and repeats	Device input can substantially understate patient phlebotomy burden	Per-draw and cumulative diagnostic blood volume with each denominator separated	Cumulative diagnostic blood loss; repeat-collection rate; transfusion exposure; patient discomfort	CLINICALLY_OR_TECHNICALLY_DERIVED	ESSENTIAL	Patient-level benefit is unproven; prospectively maintain a two-volume blood ledger	[26]
Device analytical input	Volume loaded into the cartridge, dilution, residual waste and failed-run consumption	Defines platform efficiency but not patient-level blood sparing	Loaded volume and all processing losses; never substitute for total draw	Device-input efficiency; failed/invalid sample rate	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	Draw and input are often conflated; report paired denominators	[4–7,47,50,51,72–76]
Anticoagulant, delay and temperature	Tube/additive, collection route, collection-to-assay interval and transport/storage conditions	Preanalytics may alter cells and stimulated responses	Anticoagulant and ratio; timestamps; temperature; storage; deviations	First-pass valid-run rate; sample-failure rate; repeat-collection rate	CLINICALLY_OR_TECHNICALLY_DERIVED	ESSENTIAL	No universal pediatric stability window; perform assay-specific head-to-head stability studies	[22–25]
Stimulation	Agonist, lot, dose, duration and matched controls	A functional response is specific to the applied perturbation	Stimulus identity/concentration, incubation, unstimulated and positive-control paths	Valid functional-response proportion; invalid or indeterminate rate	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	Inter-assay comparability is limited; validate reference and assay-specific protocols	[1,2,21–25]
Cell composition	Counts, recovery, viability and normalization	Mixed-cell metabolic readouts depend on cellular composition	CBC/differential, recovery, viability and normalization method	Interpretable-result rate across age/weight strata	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	Cell-specific interpretation remains incomplete; compare bulk and cell-specific measurements	[1–3]
Treatment context	Steroids, antimicrobials and immunomodulators	Treatment can confound serial immune trajectories	Drug, dose and timing relative to each sample	Protocol adherence and interpretable-sample rate under treatment exposure	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	Residual time-varying confounding; prespecify longitudinal covariates	[18]
Sample failure, repeats, and QC	Failure reason, first-pass status, repeat action, extra specimen, lot, operator, instrument, and QC flags	Technical failure can create repeat draws, added blood loss, delay, and workflow burden	Prespecified invalid-run taxonomy; first-pass yield; failure reason; repeat action; extra specimen consumed; cartridge/reagent lot; operator; instrument; calibration and internal controls; added time	First-pass success; invalid-run and repeat rates; repeat-associated blood loss; added turnaround and hands-on time	AUTHOR_PROPOSED	ESSENTIAL	No prospective pediatric-sepsis study has quantified first-pass success, invalid runs, repeat blood draws, or operator/lot effects for a nano-enabled functional assay; perform multi-lot, multi-operator bench validation followed by PICU simulation and prospective usability testing	[16,17,24,25,46,77]
Nursing and workflow burden	Hands-on time, cartridge loading, interruptions, escalation, infection-control steps, and role handoffs	Operational burden can negate analytical advantages	Task sequence; staff role; hands-on and elapsed time; interruptions; training/competency; infection-control, alert handling, and escalation records	Nursing/staff time; task completion; workflow disruption; alert/alarm burden; infection-control deviation; repeat-associated workload; patient/family acceptability	AUTHOR_PROPOSED	DESIRABLE_FOR_EARLY_FEASIBILITY; ESSENTIAL_BEFORE_IMPLEMENTATION	No assay-specific prospective PICU evidence quantifies nurse workload, repeat-associated burden, alarm load, or failure escalation; use structured workflow observation, simulation, and prospective usability testing	[16,46,77–83]
Longitudinal timing	Baseline and serial windows linked to sepsis recognition and clinical events	A trajectory requires interpretable sampling intervals	Clock time, event-linked window, missed-window reason and repeat-testing frequency	Completed planned time points; repeat draws; cumulative burden; timeliness	DIRECTLY_EVIDENCE_SUPPORTED	ESSENTIAL	Optimal schedule is unknown; compare prespecified schedules without outcome leakage	[1–3]

Notes: Low analytical device input does not automatically establish patient-level blood sparing or patient benefit. Candidate endpoints and exact endpoint sets are author-proposed evaluation measures, even when supported by cited biological, analytical, acute-care, or human-factors bridge studies. They are not demonstrated benefits, consensus standards, guideline requirements, regulatory thresholds, or validated performance targets. Provenance categories are DIRECTLY_EVIDENCE_SUPPORTED, CLINICALLY_OR_TECHNICALLY_DERIVED, and AUTHOR_PROPOSED. Essential/desirable status is an author-proposed prioritization for staged evaluation.

Abbreviations: CBC, complete blood count; PICU, pediatric intensive care unit; QC, quality control.

Traceability should cover acquisition, loading, stimulation start and stop, signal acquisition, result release, controls, and interruption type. Feasibility criteria should prespecify maximum research draw, minimum input, allowable delay, repeat conditions, and stopping rules. Reporting only successful runs obscures failure and repeat burden. Proposed thresholds remain study criteria rather than validated pediatric performance standards.^40–45

A low-input cartridge may still require a conventional collection tube, plasma separation, or a confirmatory reference assay. It may also create repeat collections when cell yield or controls fail. Conversely, a platform with a larger analytical input may reduce burden if it consolidates several measurements or accepts capillary or low-dead-space collection. Pediatric microsampling studies support the feasibility of alternative collection strategies, but they do not prove that a functional immune workflow reduces net blood loss.^26,40–45

Patient-level benefit should be assessed across the monitoring episode. Cumulative diagnostic blood loss, sample-failure rate, repeat collections, first-pass success, reference-assay draws, discomfort, access events, and staff time are candidate endpoints. Transfusion exposure may be recorded but is multifactorial and should not be attributed to one assay without an appropriate design. The denominator should include every attempted collection and run, not only successful tests.

Nano-Enabled Microfluidic Platforms and Prototype Maturity

Core nanomedicine evidence requires a demonstrated nanoscale contribution to capture, enrichment, separation, amplification, transduction, antifouling, or sample reduction.^5–7 A nanomaterial name alone is insufficient. Ordinary microfluidics remains bridge evidence when it solves a distinct workflow problem. Platforms are therefore classified as complete functional assays, partial functional modules, soluble-analyte sensors, components, or author-proposed integrations.

A complete functional chain requires a patient specimen, defined perturbation, cellular denominator or composition control, paired stimulated and unstimulated conditions, preserved response or effector capacity, and assay-validity controls. Missing perturbation or response preservation changes the claim to concentration, phenotype, capture, or transduction. Partial modules can still be valuable, but demonstrated and proposed integration must remain separate.

Nanoscale interfaces can increase binding area, organize recognition elements, or amplify binding-induced signals. Nanoplasmonic counting, phase nano-optics, field-effect transistors, and single-molecule platforms demonstrate several routes.^{5,6,50–63,84} Their analytical performance does not establish recovery from pediatric whole blood, preservation of function, antifouling, developmental interpretation, or clinical utility. Required performance should be set by the biological contrast, not the lowest reported detection limit.

A 2026 portable nanozyme-linked immunosorbent assay combined Ti₃C₂Tₓ MXene/Au nanoparticles, electrochemical readout, and machine learning to quantify multiple cytokines from approximately 30 µL of frozen plasma.¹⁷ The single-center dataset included 120 patients with sepsis immunoparalysis, 120 without, and 120 healthy participants. The reported validation set was a same-site 7:3 random holdout, not an independent external cohort. Soluble cytokine classification, even when referenced to mHLA-DR, is not an ex vivo cellular functional assay or longitudinal pediatric validation.

Recent devices add distinct components rather than a complete pediatric platform. A paper microfluidic sensor used laser-induced graphene for label-free interleukin-6 electrochemistry, while an automated digital-microfluidic cartridge reduced manual handling and reported explicit intra- and inter-cartridge variability in related pediatric critical-illness plasma (intra-cartridge coefficient of variation, 6.25%; inter-cartridge coefficient of variation, 3–21%).^46,49 Two verified 2027-assigned studies add porous-silicon-nanoparticle electrochemiluminescence and poly(ethylene glycol)-modified polydimethylsiloxane antifouling in a whole-blood extracellular-vesicle workflow.^69,71 These results support component selection and quality-control questions, not functional immune or clinical claims.

Nanomaterial-based amplification and dynamic secretion imaging answer different tasks. Aggregation-induced-emission (AIE) nanobeads can strengthen end-point interleukin-6 detection, while aptamer-functionalized nanoplasmonic arrays can image stimulus-dependent secretion in single-cell models.^4,7,64–68 The latter preserves perturbation and time-resolved output but remains a partial model-system module. Particle consistency, conjugation stability, background, cell number, and stimulated-versus-unstimulated comparability require separate control.

Cell capture and antifouling must preserve the biology being tested. High-affinity interfaces may alter activation, receptor availability, or recovery. Reports should include cell recovery, viability, interface-induced activation, nonspecific retention, matrix-matched recovery, and serial drift.^48,71 Whole-blood or plasma performance after preprocessing does not establish direct-from-blood operation, and an antifouling coating does not remove the need for matrix-specific validation.

Ordinary microfluidics can still supply essential bridge functions. Printed-circuit-board sensing informs compact routing and electronics, while recent label-free fractionation preserved platelet and leukocyte states from 100 µL of blood.^{39,47,70,74,75,85} Adult whole-blood stimulation supplies a functional reference chain, but it lacks a defined nano-enabled microfluidic module.¹⁶ These examples should not be counted as core nanomedicine evidence.

The proposed sample-to-answer architecture would divide a specimen into control and stimulated paths, regulate incubation, capture cells or products, and read paired signals. No included platform integrates that chain for pediatric sepsis. Every step consumes sample and introduces dead volume or variance. Validation must distinguish inadequate cells, failed stimulation, saturation, drift, obstruction, and data-transfer failure (Figure 3 and Table 3).

Platform comparison should therefore prioritize matrix, patient draw, device input, perturbation, cell preservation, nano function, transduction, turnaround, controls, failure status, and maturity. Table 3 presents selected exemplars by unique enabling function rather than attempting a device catalogue. The comparison keeps soluble sensing, cellular function, ordinary microfluidic bridges, and author-proposed integration visibly separate.

The choice between soluble-target and cell-resolved measurement should be explicit. Soluble sensing can be rapid and compatible with amplification, but its output integrates secretion, dilution, clearance, and cell abundance. Cell-resolved measurement can expose heterogeneity but adds capture bias, recovery requirements, and more demanding imaging or signal processing. A future cartridge may combine both, yet the validation and interpretation of each channel must remain separable.

Multiplexing expands biological coverage while increasing cross-reactivity, calibration complexity, reagent interactions, and missing-channel risk. A larger panel is not automatically more functional or more informative. Initial designs should select a minimal set with non-redundant biological roles and a prespecified interpretation for paired stimulated and unstimulated conditions. Arrays are most useful when quantitative comparability is maintained across channels, lots, matrices, and the clinically relevant range.

Temporal resolution creates a separate trade-off. Continuous secretion imaging may reveal response onset and cellular heterogeneity, while a timed end point may be simpler and more reproducible at the bedside. The intended claim should determine the architecture. Current proof-of-concept studies do not establish whether kinetics or a paired end point is clinically superior in pediatric sepsis, so both require claim-matched evaluation rather than technological preference.

Manufacturability is part of analytical validity. Nanostructure dimensions, surface functionalization, optical properties, and nanolabel loading may vary between batches and change baseline signal or gain. Reports should specify lot-release criteria, internal standards, storage, shelf life, recalibration, and allowable drift. Automated cartridges may reduce manual steps, but cartridge-to-cartridge variation, reagent filling, sealing, and reader alignment still require multi-lot and multi-operator testing.^17,46

Reference methods must match the biological claim. A soluble nano-immunoassay can be compared with a laboratory concentration assay, whereas a functional secretion claim also needs a controlled stimulation comparator and cellular denominator. Agreement should be tested in paired clinical matrices and should report disagreement, not correlation alone. Later-stage appraisal must also document nanomaterial containment, leakage, cartridge integrity, decontamination, and disposal under realistic operation. These are prospective requirements, not evidence of established pediatric safety.

Matrix performance deserves particular attention. The portable nanozyme assay reported a lower analytical limit in buffer than in plasma and required centrifuged, frozen specimens.¹⁷ The automated digital-microfluidic cartridge also used diluted, frozen plasma and showed greater variation between cartridges than within one cartridge.⁴⁶ These are informative translational findings. They show why buffer sensitivity, device input, and partial automation cannot be converted into direct-from-whole-blood performance, patient-level blood saving, or bedside readiness.

The newest 2027-assigned components reinforce the same point. Porous-silicon nanoparticles amplified electrochemiluminescence for soluble interleukins, while poly(ethylene glycol) modification reduced nonspecific adsorption in a microfluidic extracellular-vesicle workflow.^69,71 Each solves a distinct analytical problem, but neither provides controlled immune stimulation or pediatric clinical evidence. Their value in this Review is to sharpen design options and failure tests, not to enlarge an apparent count of clinically mature devices.

Translation to Pediatric Clinical Validity

Translation follows six distinct claims: analytical validation, pediatric feasibility, clinical association or validity, prediction, decision utility, and bedside implementation. Each requires a different design. Level 4 devices and pediatric association studies occupy separate positions in this sequence; narrative proximity cannot connect them.^1,5–7

Adult sepsis studies support the plausibility of repeated immune phenotyping. Longitudinal mHLA-DR and combined functional profiles change across critical illness, but adult thresholds and schedules cannot be imported into pediatrics.^{20,21,86–89} A production correction affecting two cropped panels in one mHLA-DR report was checked, and only the corrected figure informed interpretation.

Association is not prediction, particularly for secondary infection or organ dysfunction.^18,33 Positive, null, and non-transportable findings vary across assays and time points. A prediction claim requires a locked outcome, a stated origin and horizon, prespecified sampling, independent validation, calibration, and comparison with an available baseline model.

Within the verified accessible corpus through 16 September 2026, no pediatric study prospectively validated a nano-enabled functional immune assay for immunomodulatory treatment selection or bedside decision utility. An adjacent PICU study used serial procalcitonin with clinical and microbiological reassessment to guide antimicrobial de-escalation.⁹⁰ Its prospective cohort with a historical comparator shows that a biomarker-linked workflow can be evaluated, but it does not validate functional immunity, immunomodulatory selection, or nanomedicine.

Apparent maturity also depends on cohort independence. Linked functional-profiling reports, secondary analyses, assay comparisons, and related immunosuppressive-phenotype or mHLA-DR-targeting reports may answer different questions but cannot be counted as independent replications.^{22–25,91–93} Each cohort or version family therefore contributes once to evidentiary weight. Intended use must be stated before choosing a threshold: monitoring change, enriching a trial, predicting an outcome, and guiding a decision are different claims.

Clinical validity should be tested against a claim-matched reference. A phenotype-related assay must state whether it reproduces mHLA-DR, measures inducible response, or adds a complementary domain. A functional cytokine claim requires a controlled stimulation comparator before outcome associations are emphasized.^1,5 Timing, disagreement, treatment exposure, and competing risks must be reported rather than resolved by relabelling measurements as one immune state.

Decision utility is the final and most demanding stage. The result must be timely, interpretable, actionable, and linked to a prospectively tested management pathway with safety, adherence, workload, and patient-relevant outcomes. A valid measurement may still fail because no action is defined or burden outweighs information gain. The current gap is an uncompleted validation stage, not evidence against the technological concept (Table 5).

Table 5.

Author-Proposed Staged Validation Roadmap for Nano-Enabled Functional Immunomonitoring

Stage	Validation Question	Provenance/Priority	Evidence Lane	Maturity/Certainty	Currently Allowable Claim Class	Minimum Entry/Progression Condition	Evaluation Endpoint	Failure Criterion/Stopping Rule	Human/AI Boundary	References
1. Analytical/biological verification	Does the assay reproducibly measure the intended analyte or functional response while preserving the required functional chain?	AUTHOR_PROPOSED; ESSENTIAL	Predominantly Level 4	Component-level analytical evidence; integrated pediatric certainty absent	Analytical detectability, precision and assay-validity claims only	Prespecified matrix, interference, precision, recovery, stability, lot, control and invalid-run criteria met	Accuracy, precision, recovery, analytical range, stability, functional-chain preservation and invalid-run rate	Stop or redesign for unstable response, failed controls, unacceptable interference, drift or nonreproducibility	Laboratory/engineering review; no clinical recommendation	[4–7,16,17,39,46,47,49–51,69,71–76]
2. Pediatric feasibility/developmental calibration	Can serial testing be completed safely, reliably and on time across pediatric developmental strata?	AUTHOR_PROPOSED; ESSENTIAL	Levels 1 and 3 with Level 4 device support	Direct pediatric biology is concentrated; integrated feasibility remains unvalidated	Pediatric feasibility, blood-accounting and developmental-calibration claims	Stage 1 criteria met; prospective protocol, two-volume blood ledger, age strata, workflow and failure rules prespecified	Successful-run proportion, total draw and device input, repeat draws, turnaround, discomfort and protocol deviations	Stop for excessive failure/repeat draw, unacceptable cumulative burden, age-dependent failure or infeasible workflow	Nurse/operator perform QC and escalation; human review mandatory	[26,39,46,74,75]
3. Clinical validity	Are measurements associated reproducibly with prespecified pediatric immune states or outcomes in independent cohorts?	AUTHOR_PROPOSED; ESSENTIAL	Level 1 with bounded Level 2 bridges	Two confirmed in-scope independent pediatric cohort units; one additional Nationwide family overlap-uncertain; Fisler <21-year Northwell cohort scope-limited and not counted	Cohort-bounded association or clinical-validity claims	Stage 2 criteria met; independent multicenter cohort, cohort-family control, developmental adjustment and prespecified analysis	Association, calibration, subgroup performance, reproducibility and negative/conflicting evidence	Stop or narrow claim for failed independent replication, unstable calibration, major subgroup error or cohort dependence	Clinician interpretation only; no treatment recommendation	[1–3,18–22,27,31]
4. Prediction	Does a locked model predict a prespecified candidate endpoint early enough for intended evaluation?	AUTHOR_PROPOSED; ESSENTIAL only for a prediction claim	Level 1 target evidence with contextual analytic bridges	Within the verified accessible corpus through 16 September 2026, no prospectively validated pediatric longitudinal immune-function trajectory model identified	Prospective prediction claim for the prespecified endpoint only	Stage 3 validity met; locked model, temporal/external validation, leakage controls, uncertainty and comparator prespecified	Calibration, discrimination, lead time, subgroup error and prospective silent-evaluation performance	Stop for leakage, poor calibration/transportability, unstable subgroup performance or no useful lead time	Model output cannot replace observed measurements; clinician review and override required	[17,33,34,37,94]
5. Decision utility	Does use of the result improve a prespecified clinical decision without unacceptable harm or burden?	AUTHOR_PROPOSED; ESSENTIAL only for a decision-utility claim	Implementation/context and adjacent pediatric decision bridges; direct functional-immune utility evidence absent	No prospectively validated nano-enabled functional-immune decision pathway; adjacent biomarker stewardship does not establish this use case	Decision-process or utility claim for the evaluated use only	Stage 4 prediction met where applicable; action pathway, comparator, safety rules and prospective impact design prespecified	Decision quality, timeliness, safety, adherence, workload, alert burden and patient-relevant outcome	Stop for no incremental benefit, safety signal, automation bias, inequity, excessive burden or override failure	Human authorization controls all action; AI has no autonomous treatment role	[28–30,90]
6. Bedside implementation	Can the assay, display and review pathway operate reliably and equitably in PICU workflow?	AUTHOR_PROPOSED; ESSENTIAL before deployment	Level 3/context implementation bridges plus prior-stage evidence	Transfer principles only; no validated PICU immune-assay dashboard or deployment pathway	Assay-specific implementation claim within the tested setting	Relevant prior-stage criteria met; human-factors, downtime, infection-control, training, escalation and auditability plans prespecified	Comprehension, first-pass success, timeliness, workload, alarm burden, downtime, equity and protocol adherence	Stop or redesign for unsafe escalation, unmanageable burden, recurrent invalid runs, poor comprehension or inequitable access	Observed data remain inspectable; uncertainty visible; human review, override and documented action outside the automated model	[16,28–30,46,77–83]

Notes: This is an author-proposed staged validation roadmap, not a consensus standard, clinical guideline, regulatory pathway or validated deployment pathway. No stage automatically proves or authorizes the next. Evidence lane describes translational proximity; maturity/certainty is appraised independently. Negative evidence, cohort independence, prespecified evaluation, external validation, multicenter replication, failure criteria and human review remain mandatory where applicable. AI is restricted to analysis, uncertainty-aware display and evaluation; it cannot autonomously diagnose, select treatment or direct immunotherapy.

Abbreviations: AI, artificial intelligence; PICU, pediatric intensive care unit.

Clinical-validity studies should compare the new platform with relevant alternatives rather than evaluate it in isolation. For an mHLA-DR-related claim, investigators must state whether the new assay measures the same phenotype, a stimulation response, or a complementary state. For a functional cytokine claim, agreement with a laboratory stimulation method should precede emphasis on outcome associations. Discordance may reveal added biology, but it may also expose matrix, cell-count, or preanalytical error.^{1,5,20,21,95–99}

Outcome timing changes the meaning of a result. A measurement obtained after prolonged critical illness may associate with nosocomial infection for reasons different from an early assessment. A trajectory derived with future samples cannot be presented as a real-time predictor. Analyses should define the prediction origin, available history, horizon, competing events, and actions already taken. These requirements apply equally to conventional assays and nano-enabled platforms.^2,35

Longitudinal Immune Trajectories and AI Validation

An immune trajectory is a time-ordered series of defined immune measurements, not a label attached to any repeated laboratory value. Adult longitudinal multi-omic analysis identified three temporal immune states and showed partial asynchrony between clinical time and immune state.³⁴ This informs trajectory architecture but does not establish pediatric features, thresholds, prediction, or treatment selection. Direct pediatric functional series remain small and exploratory.²

A developmentally calibrated analysis must separate between-child differences from within-child change. Candidate covariates include age, sepsis timing, treatment, pathogen group, cell composition, assay quality, sampling interval, and missingness.^18,19,31 These variables should be declared before modeling, and an age-neutral threshold must not be presented as pediatric biology.

AI remains a bounded analytical layer. It may estimate age-adjusted reference distributions, represent irregular series, combine labeled but non-equivalent modalities, and quantify uncertainty. The derived state is not directly measured. It must not autonomously diagnose immunoparalysis, recommend immunotherapy, or hide missingness in a single score. Adult clustering and same-site holdout classification are computational bridges, not pediatric longitudinal validation.^17,94,100

Development, internal validation, and external validation must be separated. All samples from one child and linked cohort families belong in one partition. Inputs must be available at the stated prediction time. Reports should address calibration, uncertainty coverage, developmental subgroup performance, missing-data degradation, and a simple comparator. Pediatric transcriptomics can inform candidate features but cannot replace independent validation.^19,101,102

Physiological trajectories provide adjunct context. A multicenter study developed and validated temperature trajectories in 11,566 children across 13 PICUs; hypothermic trajectories were associated with worse outcomes.³⁷ This is direct pediatric physiological evidence, not a longitudinal immune-function model. Tele-ICU and remote-monitoring studies likewise inform data routing and display rather than cellular measurement.^28,29

A reviewable display should separate observations from derived estimates and show age context, recent direction, assay validity, missingness, and uncertainty. Explainability should identify the measured domain and whether an output is descriptive or predictive. Clinicians and nurses must be able to inspect source measurements. Human-factors evaluation tests comprehension and workflow, not validity of the immune-state construct.^19,28

Prospective evaluation should compare an AI-assisted display with a simpler age-adjusted longitudinal representation. Complexity is justified only by improved calibration, comprehension, timeliness, or decision consistency without unacceptable subgroup error. Models must be locked, human review and override defined, and prediction tested separately from decision utility (Figure 4 and Table 5).

Figure 4.

Proposed evaluation framework for AI-assisted longitudinal immunomonitoring. (A) Serial immune measurements retain timing and quality metadata; physiological streams are adjuncts.^{1–3,20,21,33,34,37,101} (B) Validity, missingness, temporal alignment, development, treatment, sampling context, and data leakage are checked before modeling.^{19,94,100–102} (C) Derived estimates display uncertainty and remain linked to observations. The nano-cytokine study illustrates same-site random-holdout classification, not external or longitudinal validation.¹⁷ (D) Prediction requires locked endpoints, patient-level partitioning, independent validation, calibration, and subgroup-error assessment. Within the verified accessible corpus through 16 September 2026, no qualifying pediatric longitudinal immune-function model has been prospectively validated. Secondary infection remains a candidate endpoint. (E) Nurses, clinicians, laboratory staff, and engineers review observed, invalid, derived, and uncertain information. Human-factors and ICU-interface studies inform evaluation, not immune-model validity.^{77–83,103–107} All clinical action remains human-authorized.

Interpretable summaries should precede complex models. Candidate outputs include baseline-adjusted change, rate of change, persistence, recovery, and discordance between domains. Each requires uncertainty linked to sampling density and assay quality. Irregular intervals, informative missingness, and changing treatment complicate apparent trajectories. A model that smooths these features without showing their influence may produce an attractive curve while obscuring the evidence available at each time point.

Leakage control is essential because each child contributes several observations. Samples from one child and linked cohort families must remain within one development or validation partition. Future values, discharge labels, retrospectively adjudicated variables, or preprocessing fitted on the entire dataset create look-ahead bias. The analysis plan should prespecify eligible time points, quality exclusions, missing-data handling, feature construction, and the simple comparator before model fitting.

PICU Deployment, Nursing Workflow, and Human Factors

Current implementation evidence supplies transfer principles, not validation of this assay in a PICU. The workflow begins with sampling-window selection, access route, treatment recording, discard-volume accounting, path labelling, and deviation documentation. Device-side tasks include preparation, lot and expiry checks, specimen adequacy, timing, controls, invalid-run recognition, infection control, disposal, and escalation.^28,78–82

Nurses are central to future assay-specific testing because sampling, cartridge operation, quality control, alarm response, and communication compete with bedside care. The first defensible use case is serial descriptive functional-state monitoring: a nurse or trained operator collects a prespecified small-volume specimen, documents total draw and device input, runs paired unstimulated and controlled-stimulation pathways when required, and releases only assay-valid domain-specific results with visible quality flags. Invalid or discordant runs trigger protocolized repeat or confirmatory review rather than an automated alert. The clinical team reviews observed results, uncertainty, age and treatment context, and trends; no cartridge or model output autonomously diagnoses sepsis, selects immunotherapy, or directs treatment. Outcomes should include hands-on time, interruptions, training burden, workarounds, invalid runs, repeat draws, and clarification of alerts. A dashboard must distinguish measured values from model-derived interpretation and display assay quality and uncertainty.

Alarm design requires restraint. Physiological streams are frequent, while immune measurements are intermittent and may follow stimulation delay. Escalation logic should identify whether a notification reflects a new result, a derived trend, a quality-control failure, or physiological deterioration. Wearable and remote signals remain adjunctive. Alarm-fatigue evidence guides interface design but does not establish immune-assay utility.^{28–30,78–82,103}

Candidate endpoints include comfort, family acceptability, cumulative blood drawn, first-pass success, timeliness, workload, alarm burden, access equity, and adherence to escalation.^{77,83,103,104} Training studies should record time to competency, error types, retraining, and performance across shifts. Workflow simulation and prospective usability testing should precede any care pathway in which an immune result influences action.

Assay-specific testing must define ownership. Protocols should state who reviews measured and derived outputs, responds to invalid runs, acknowledges alerts, communicates uncertainty, and documents action. Cumulative phlebotomy, failed collections, repeats, discomfort, and staff time should be measured alongside device input. These remain proposed implementation requirements, not evidence of current deployment.^105–107

Integration with laboratory information systems and the electronic health record requires a defined data path. Raw signal, processed result, control status, timestamps, device and reagent lot, operator, and correction or repeat events should remain auditable Downtime procedures must specify whether a biological incubation can continue, whether the result becomes invalid, and how the team is notified. Cybersecurity and interoperability are implementation requirements, but neither validates the immune construct.

Patient and family experience should be evaluated separately from technical success. Repeated access, line manipulation, capillary collection, waiting time, discomfort, and explanations of uncertain results may influence acceptability. A smaller cartridge does not guarantee less discomfort, and faster output may increase rather than reduce communication burden. Participatory evaluation should include nurses, physicians, laboratory and informatics staff, and families where appropriate.

Future Perspectives

The first stage is analytical and biological verification of the nano-enabled module. The intended measurand, nanoscale function, matrix, perturbation, reference method, precision, recovery, interference, drift, cell recovery, viability, and failure modes must be prespecified.^4–7 The second stage is pediatric feasibility and developmental calibration, including age coverage, neonatal separation, total draw, device input, repeats, serial timing, treatment metadata, and workflow.

The third stage is clinical validity. The immune construct and measurement class must be prespecified, and negative or discordant findings retained.^1,19 Agreement with phenotype does not prove equivalent function. Independent multicenter cohorts should test reproducibility, center-level calibration, age-specific uncertainty, and cohort-family independence before any predictive model is optimized.

Prediction is a fourth stage only when it matches the intended use. Outcomes, origin, horizon, competing risks, and validation must be locked.^23,31,33 The fifth stage tests decision utility through a prospective assay-informed pathway with a comparator, human review, permitted action, safety rules, adherence, contamination, workload, and patient-relevant outcomes. The sixth stage evaluates bedside reproducibility, competency, downtime, invalid-run response, alarm burden, equity, and infection control.

Three priorities follow. First, standardize reporting of patient draw, device input, and measurement class. Second, establish multicenter pediatric serial datasets with functional assays and developmental calibration. Third, evaluate integrated cartridges through sequential claim stages. Each roadmap item should identify its provenance as evidence-supported, technically derived, or author-proposed. A common dataset should link raw signals, quality events, failures, references, treatments, outcomes, and cohort-family identifiers (Figures 3 and 4, Table 4 and Table 5).

Stopping rules should govern every transition. Unstable analytical performance, excessive invalid or repeat-draw rates, failed independent replication, poor calibration or transportability, unsafe escalation, or no incremental utility should prevent progression. Explicit failure criteria give negative evidence a constructive role and reduce selective continuation. Pediatric functional-immune prediction and immunomodulatory decision utility remain unvalidated.

Multicenter studies should test transportability rather than merely enlarge a pooled sample. Centers differ in developmental case mix, treatment, collection route, handling time, staffing, and laboratory infrastructure. External validation should therefore report center-level calibration, invalid-run patterns, age-specific uncertainty, and reproducibility of the assay definition. Device redesign or threshold updating after external evaluation must be documented as a new version requiring renewed validation.

A minimum dataset should link patient and sample identifiers, collection context, device and reagent lots, raw and processed signals, controls, failures, reference results, treatments, outcomes, and cohort-family identifiers. Provenance should distinguish directly supported variables from technically derived requirements and author-proposed endpoints. This structure enables reanalysis and negative-result reporting, but it does not itself establish a clinical claim.

Study design should also change with the claim stage. Discovery cohorts can explore domains and timing, but analytical transfer requires locked protocols and reference comparisons. Pediatric feasibility requires prospective blood and workflow accounting. Clinical validity requires independent cohorts and prespecified confounders. Prediction requires temporal and external validation. Decision utility requires a prospective impact design with a defined action pathway. Combining these objectives in one underpowered study would obscure which stage failed.

Implementation research should begin before the final device is fixed, but it must not substitute for assay validity. Early simulations can identify loading errors, unclear controls, infection-control problems, display ambiguity, and unrealistic staffing assumptions. Later silent deployment can test data flow, turnaround, downtime, and alert behavior without influencing care. Only after those stages should an assay-informed pathway be evaluated for clinical impact under human authorization.

Conclusion

Nano-enabled microfluidics can advance pediatric sepsis immunomonitoring only when each nanoscale function serves a defined biological task. Current platforms demonstrate capture, amplification, transduction, automation, or antifouling, but not an integrated pediatric functional assay.^{4–7,17,46,49,69,71}

Pediatric evidence supports developmental heterogeneity, separable functional and metabolic domains, and the feasibility of serial physiological or soluble-biomarker assessment. Within the verified accessible corpus through 16 September 2026, it does not establish a validated longitudinal immune-function model, nano-enabled bedside assay, or immunomodulatory decision pathway. Adult, related-pediatric, and engineering evidence define mechanisms and validation requirements, not pediatric clinical utility.

The defensible path is sequential. Studies should preserve the functional chain, verify patient-level blood saving and developmental calibration, and reproduce clinical validity in independent multicenter cohorts. Prediction or decision utility should be tested only afterward. AI and physiological signals may support interpretation, but observed measurements, uncertainty, workflow, and all clinical actions must remain visible to human reviewers.

This program treats negative results as informative. Failure to preserve cellular response, reduce patient-level blood burden, transport across ages or centers, or improve a defined decision should narrow the intended use or stop development. Such stopping rules are essential if technical novelty is to mature into a safe and credible pediatric monitoring pathway.

Funding Statement

This work received no specific funding.

Declaration of AI Use

During the preparation of Figures 1–4, Qi Zhang used Google DeepMind Nano Banana Pro (Gemini 3 Pro Image; stable model identifier gemini-3-pro-image) between 15 and 20 July 2026 solely to prepare preliminary conceptual sketches from author-defined scientific specifications. Qi Zhang subsequently redrew the final figures using Microsoft PowerPoint and/or BioRender, as applicable. All authors reviewed and approved the final figures and this disclosure, verified the labels, relationships, citations, and evidence boundaries, and accept responsibility for the scientific accuracy, integrity, and suitability of the final figures for publication. The final figures are original author-controlled compositions submitted with the authors’ permission for publication; no third-party artwork is reproduced or adapted. No patient data, research results, clinical samples, or diagnostic images were used.

Abbreviations

AIE, aggregation-induced emission; AI, artificial intelligence; CHOP, Children’s Hospital of Philadelphia; IEEE, Institute of Electrical and Electronics Engineers; LPS, lipopolysaccharide; MEDLINE, Medical Literature Analysis and Retrieval System Online; mHLA-DR, monocyte human leukocyte antigen-DR; PICU, pediatric intensive care unit; SERS, surface-enhanced Raman scattering; tele-ICU, tele-intensive care unit; TNF-α, tumor necrosis factor alpha.

Data Sharing Statement

No new participant-level datasets were generated for this Review. The evidence base consists of the cited publications. The reproducible search strategy, search log, and sanitized claim–evidence traceability materials are provided in the combined Supplementary Material.

Ethics and Consent

Ethics approval and participant consent were not required because this article is a literature review and did not involve new recruitment, intervention, or collection of identifiable participant data. Consent for publication is not applicable.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.