Tissue-Specific Immunopathology in Fatal COVID-19
Bibliographic record
Abstract
ABSTRACT Background Tissue inflammation is associated with organ dysfunction and death in Covid-19. The efficacy of dexamethasone in preventing mortality in critical Covid-19 suggests that inflammation has a causal role in death. Whether this deleterious inflammation is a direct response to the presence of SARS-CoV-2, or an independent immuno-pathologic process, is unknown. Methods Tissue was acquired from detailed post-mortem examinations conducted on 11 well characterised hospitalised patients with fatal Covid-19. SARS-CoV-2 organotropism was mapped at an organ level by multiplex PCR and sequencing, with cellular resolution achieved by in situ viral spike (S) protein detection. Histological evidence of inflammation and organ injury was systematically examined, and the pulmonary immune response characterized with multiplex immunofluorescence. Findings SARS-CoV-2 was detected across a wide variety of organs, most frequently in the respiratory tract but also in numerous extra-pulmonary sites. Minimal histological evidence of inflammation was identified in non-pulmonary organs despite frequent detection of viral RNA and protein. At a cellular level, viral protein was identified without adjacent inflammation in the intestine, liver and kidney. Severe inflammatory change was restricted to the lung and reticulo-endothelial system. Diffuse alveolar damage, pulmonary thrombi and a monocyte/myeloid-predominant vasculitis were the predominant pulmonary findings, though there was not a consistent association between viral presence and either the presence or nature of the inflammatory response within the lung. Immunophenotyping revealed an influx of macrophages, monocytes and T cells into pulmonary parenchyma. Bone marrow examination revealed plasmacytosis, erythroid dysplasia and iron-laden macrophages. Plasma cell excess was also present in lymph node, spleen and lung. These stereotyped reticulo-endothelial responses occurred largely independently of the presence of virus in lymphoid tissues. Conclusions Tissue inflammation and organ dysfunction in fatal Covid-19 do not map to the tissue and cellular distribution of SARS-CoV-2, demonstrating tissue-specific tolerance. We conclude that death in Covid-19 is primarily a consequence of immune-mediated, rather than pathogen-mediated, organ inflammation and injury. Funding The Chief Scientist Office, LifeArc, Medical Research Scotland, UKRI (MRC).
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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 | 0.001 | 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".