A deficient immune response to SARS-CoV-2 in the nasopharynx is associated with severe COVID-19 pneumonia
Bibliographic record
Abstract
OBJECTIVES: We analyzed the expression of inflammatory and antiviral genes in the nasopharynx of SARS-CoV-2 infected patients and their association with the severity of COVID-19 pneumonia. METHODS: We conducted a cross-sectional study on 223 SARS-CoV-2 infected patients. Clinical data were collected from medical records, and nasopharyngeal samples were collected in the first 24 hours after admission to the emergency room. The gene expression of eight proinflammatory/antiviral genes (plasminogen activator urokinase receptor [PLAUR], interleukin [IL]-6, IL-8, interferon [IFN]-β, IFN-stimulated gene 15 [ISG15], retinoic acid-inducible gene I [RIG-I], C-C motif ligand 5 [CCL5], and chemokine C-X-C motif ligand 10 [CXCL10]) were quantified by real-time polymerase chain reaction. Outcome variables were: (i) pneumonia; (ii) severe pneumonia or acute respiratory distress syndrome. Statistical analysis was performed using multivariate logistic regression analyses. RESULTS: We enrolled 84 mild, 88 moderate, and 51 severe/critical cases. High expression of PLAUR (adjusted odds ratio [aOR] = 1.25; P = 0.032, risk factor) and low expression of CXCL10 (aOR = 0.89; P = 0.048, protective factor) were associated with pneumonia. Furthermore, lower values of ISG15 (aOR = 0.88, P = 0.021), RIG-I (aOR = 0.87, P = 0.034), CCL5 (aOR = 0.73, P <0.001), and CXCL10 (aOR = 0.84, P = 0.002) were risk factors for severe pneumonia/acute respiratory distress syndrome. CONCLUSION: An unbalanced early innate immune response to SARS-CoV-2 in the nasopharynx, characterized by high expression of PLAUR and low expression of antiviral genes (ISG15 and RIG-I), and chemokines (CCL5 and CXCL10), was associated with COVID-19 severity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".