Airway epithelial SARS-CoV-2 infectious and repair responses: relationships to age, sex, and post-COVID pulmonary syndromes
Bibliographic record
Abstract
Abstract The long-term pulmonary sequelae of SARS-CoV-2 respiratory infections reflect infection severity, innate and adaptive immunity, and respiratory epithelial repair. This study investigated the acute and reparative responses as a function of age and sex in primary human bronchial epithelial (HBE) cultures utilizing a 14-day SARS-CoV-2 infection protocol. SARS-CoV-2 infection peaked at 3 days post-infection (dpi) with an ∼ 2 log titer suppression at 14 dpi. SARS-CoV-2 infection induced interferon, interferon-induced gene, and cell damage responses. No age- or sex-dependent effects on SARS-CoV-2 infection were detected. Airway epithelia repaired to an abnormal mucus metaplastic/inflammatory state that reflected potentially beneficial and adverse consequences at 14 dpi. Repair processes were infection severity-dependent, not sex-dependent, and were more robust in young donor cultures. Analyses of long-COVID subjects with persistent pulmonary fibrosis or persistent bronchitic airway diseases exhibited expression of HBE 14 dpi failed repair gene signatures, including ISG gene signatures. Human airway epithelial repair post-SARS-CoV-2 is prolonged and incomplete in vitro over 14 days, and persistently abnormal repair may contribute to phenotypes of people with long-COVID pulmonary syndrome.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".