Toward precision medicine: Inflammatory nasal epithelial transcriptomic profiles in long COVID
Bibliographic record
Abstract
Rationale and objectives Little is known about the role of the nasal epithelium in long COVID. This study aimed to assess nasal epithelial transcriptomes of long COVID(LC) patients to unravel pathophysiological mechanisms for disease management. Methods Medical data and transcriptomes were obtained from participants in the ‘Precision Medicine for more Oxygen'(P4O2) COVID-19 cohort, at 3-6(n=40) and 12-18(n=15) months post-COVID. Cell type frequencies were estimated by deconvolution from a single-cell dataset. Hierarchical clustering identified transcriptomic clusters and cellular clusters from which differences in gene expression, gene set enrichment and pulmonary phenotypes were assessed. Functional validation was performed using CRISPR-Cas9 gene editing and in vitro assays in primary mutant nasal epithelium and gene expression comparisons were made to healthy controls(n=51). Results At 3-6 and 12-18 months, transcriptomes associated with inflammatory pathways(padj<0.05). Transcriptomic and cellular clusters were identified and were related to inflammation and ciliogenesis(padj<0.05). Comparison of transcriptomes of patients with and without pulmonary radiological abnormalities resulted in 613 significantly differentially expressed genes(padj<0.05). Upregulated inflammatory genes were observed in patients with abnormalities. SMURF1 expression was significantly increased in patients with compared to those without abnormalities and healthy controls. SMURF1 -/- mutant nasal epithelial cells produced significantly lower levels(p<0.05) of pro-inflammatory cytokines upon virus exposure compared to controls. Conclusion Nasal epithelium in LC exhibits persistent inflammatory states. SMURF1 upregulation potentially contributes to an exacerbated inflammatory state in nasal epithelium of patients with radiological abnormalities. This study demonstrates the importance of understanding these inflammatory profiles within a clinical context and emphasizes the need for further assessment and validation of SMURF1 's role in LC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".