Immune and epithelial determinants of age-related risk and alveolar injury in fatal COVID-19
Bibliographic record
Abstract
Respiratory failure in COVID-19 is characterized by widespread disruption of the lung's alveolar gas exchange interface. To elucidate determinants of alveolar lung damage, we performed epithelial and immune cell profiling in lungs from 24 COVID-19 autopsies and 43 uninfected organ donors ages 18-92 years. We found marked loss of type 2 alveolar epithelial (T2AE) cells and increased perialveolar lymphocyte cytotoxicity in all fatal COVID-19 cases, even at early stages before typical patterns of acute lung injury are histologically apparent. In lungs from uninfected organ donors, there was also progressive loss of T2AE cells with increasing age, which may increase susceptibility to COVID-19-mediated lung damage in older individuals. In the fatal COVID-19 cases, macrophage infiltration differed according to the histopathological pattern of lung injury. In cases with acute lung injury, we found accumulation of CD4+ macrophages that expressed distinctly high levels of T cell activation and costimulation genes and strongly correlated with increased extent of alveolar epithelial cell depletion and CD8+ T cell cytotoxicity. Together, our results show that T2AE cell deficiency may underlie age-related COVID-19 risk and initiate alveolar dysfunction shortly after infection, and we define immune cell mediators that may contribute to alveolar injury in distinct pathological stages of fatal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".