Tissue-Specific Immunopathology in Fatal COVID-19
Bibliographic record
Abstract
Abstract Rationale In life-threatening coronavirus disease (COVID-19), corticosteroids reduce mortality, suggesting that immune responses have a causal role in death. Whether this deleterious inflammation is primarily a direct reaction to the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or an independent immunopathologic process is unknown. Objectives To determine SARS-CoV-2 organotropism and organ-specific inflammatory responses and the relationships among viral presence, inflammation, and organ injury. Methods Tissue was acquired from 11 detailed postmortem examinations. SARS-CoV-2 organotropism was mapped by using multiplex PCR and sequencing, with cellular resolution achieved by in situ viral S (spike) protein detection. Histologic evidence of inflammation was quantified from 37 anatomic sites, and the pulmonary immune response was characterized by using multiplex immunofluorescence. Measurements and Main Results Multiple aberrant immune responses in fatal COVID-19 were found, principally involving the lung and reticuloendothelial system, and these were not clearly topologically associated with the virus. Inflammation and organ dysfunction did not map to the tissue and cellular distribution of SARS-CoV-2 RNA and protein between or within tissues. An arteritis was identified in the lung, which was further characterized as a monocyte/myeloid-rich vasculitis, and occurred together with an influx of macrophage/monocyte-lineage cells into the pulmonary parenchyma. In addition, stereotyped abnormal reticuloendothelial responses, including excessive reactive plasmacytosis and iron-laden macrophages, were present and dissociated from viral presence in lymphoid tissues. Conclusions Tissue-specific immunopathology occurs in COVID-19, implicating a significant component of the immune-mediated, virus-independent immunopathologic process as a primary mechanism in severe disease. Our data highlight novel immunopathologic mechanisms and validate ongoing and future efforts to therapeutically target aberrant macrophage and plasma-cell responses as well as promote pathogen tolerance in 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 | 0.000 |
| 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".