Characterization of CXCL10 as a biomarker of respiratory tract infections detectable by open-source lateral flow immunoassay
Bibliographic record
Abstract
ABSTRACT Understanding core mechanisms common to respiratory tract viral pathogenesis and host-responses to infections may provide biomarkers for at-risk patient populations that guide interventions aimed at reducing morbidity, mortality, and economic costs. Secreted interferon stimulated gene protein products including CXCL10, CXCL11, and TNFSF10 could provide early biomarker signals that are prognostic for respiratory tract viral infections. In the present study, we had the overarching goal of defining the expression patterns of CXCL10, CXCL11, and TNFSF10 in clinical respiratory mucosal samples for multiple respiratory tract infections including respiratory syncytial virus, rhinovirus, influenza A and SARS-CoV-2 to inform the development of a host-biomarker point of care lateral flow immunoassay tool. Gene expression levels from upper airway samples suggested that CXCL10 and CXCL11 elevations were consistent across multiple viruses, correlated with higher SARS-CoV-2 viral load, and had a lower variance over the course of COVID-19 infection compared to TNFSF10 . Deep proteomic profiling using mass-spectrometry revealed CXCL10 protein was not detectable in oral samples from healthy individuals. CXCL10 levels were measured from the saliva of SARS-CoV-2 infected individuals and showed significant elevations in CXCL10 protein concentration. A prototype lateral flow immunoassay for detecting CXCL10 protein with a sensitivity of 2ng/mL in human saliva is presented. Our work provides a foundation for further exploration of CXCL10 as a host biomarker relevant in respiratory tract viral infections. Leveraging lateral flow immunoassay technology for detection of biomarkers prognostic of respiratory tract infection may provide opportunities to intervene selectively and aggressively in those most at risk of poor outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".