Validation of CXCL10 as a biomarker of respiratory tract infections detectable by lateral flow immunoassay
Bibliographic record
Abstract
Introduction: Biomarkers of respiratory tract infections historically focused on the etiological cause of infection, although much of the morbidity and mortality is driven by the host-pathological response. Aim: Determine host biomarkers indicative of viral respiratory tract infections that are amenable to lateral flow immunoassay (LFIA) testing. Methods: Datamining was performed on in-house and publicly available datasets from respiratory syncytial virus (RSV), rhinovirus, influenza A and SARS-CoV-2 infected patient nasopharyngeal swab samples and compared to healthy controls. CXCL10, CXCL11 and TNFSF10 gene expression levels were assessed and a correlation analysis was performed in relation to infection severity and time-course. Lastly, the signature was validated at the protein level in saliva as a prerequisite for development of a host-response LFIA. Results:CXCL10 and CXCL11 upregulation was positively correlated with RSV when compared to control (p= 0.016, p= 0.006). No significant association was found with influenza A or rhinovirus for all three genes. CXCL10/CXCL11/TNFSF10 upregulation was positively correlated with SARS-CoV-2 infection when compared to control (p < 0.001). CXCL10 expression correlated with COVID-19 severity and had the lowest variance over infection time-course. CXCL10 was not detected at the protein level in healthy saliva but was elevated in saliva from COVID-19 patients. A CXCL10 LFIA was developed with a sensitivity of 2 ng/ml in a buffer and artificial saliva. Conclusion: The findings validate the potential utility of examining host immune responses during viral respiratory tract infections by exploring CXCL10 as a biomarker detectable by LFIA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".