Interleukin-17, a salivary biomarker for COVID-19 severity
Bibliographic record
Abstract
OBJECTIVES: T-helper 17 cell-mediated response and their effector IL-17 cytokine induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a major cause of COVID-19 disease severity and death. Therefore, the study aimed to determine if IL-17 level in saliva mirrors its circulatory level and hence can be used as a non-invasive biomarker for disease severity. METHODS: Interleukin-17 (IL-17) level was evaluated by ELISA in saliva and blood of 201 adult COVID-19 patients with different levels of severity. The IL-17 saliva level was also associated with COVID-19 disease severity, and need for mechanical ventilation and/or death within 29 days after admission of severe COVID-19 patients. RESULTS: We found that IL-17 level in saliva of COVID-19 patients reflected its circulatory level. High IL-17 level in saliva was associated with COVID-19 severity (P<0.001), need for mechanical ventilation (P = 0.002), and/or death by 29 days (P = 0.002), after adjusting for patients' demographics, comorbidity, and COVID-19 serum severity markers such as D-Dimer, C-reactive protein, and ferritin. CONCLUSION: We propose that saliva IL-17 level could be used as a biomarker to identify patients at risk of developing severe COVID-19.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".