Validation of two multiplex platforms to quantify circulating markers of inflammation and endothelial injury in severe infection
Bibliographic record
Abstract
Biomarkers can prognosticate outcome and enable risk-stratification. In severe infection, focusing on multiple markers reflecting pathophysiological mechanisms of organ injury could enhance management and pathway-directed therapeutics. Limited data exist on the performance of multiplex biomarker platforms. Our goal was to compare endothelial and immune activation biomarkers in severe pediatric infections using two multiplex platforms. Frozen plasma from 410 children presenting to the Jinja Regional Hospital in Uganda with suspected infection was used to measure biomarkers of endothelial (Angiopoietin-2, sFlt-1, sVCAM-1, sICAM-1) and immune (IL-6, IP-10, sTNFR-1, CHI3L1) activation. Two multiplex platforms (Luminex®, EllaTM) based on monoclonal antibody sandwich immunoassays using biotin-streptavidin conjugate chemistry were selected with reagents from R&D Systems. The two platforms differed in ease and time of completion, number of samples per assay, and dynamic concentration range. Intra-assay variability assessed using a coefficient of variation (CV%) was 2.2-3.4 for Luminex® and 1.2-2.9 for EllaTM. Correlations for biomarker concentrations within dynamic range of both platforms were best for IL-6 (ρ = 0.96, p<0.0001), IP-10 (ρ = 0.94, p<0.0001) and sFlt-1 (ρ = 0.94, p<0.0001). Agreement between concentrations obtained by both methods assessed by the Bland-Altman test varied, with best agreement for CHI3L1. Our data suggest that biomarkers of endothelial and immune activation can be readily measured with multiplex platforms. Luminex® and EllaTM produced reliable results with excellent CV% values. The EllaTM platform was more automated and completed in 75 minutes, potentially compatible with near-patient use. Trends in concentrations obtained by these methods were highly correlated, although absolute values varied, suggesting caution is required when comparing data from different multiplex platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".