Pathogenesis of COVID-19; Acute Auto-inflammatory Disease (Endotheliopathica & Leukocytoclastica COVIDicus)
Bibliographic record
Abstract
BACKGROUND: The pathogenesis of the COVID19 pandemic, that has killed one million nine hundred people and infected more the 90 million until end of 2020, has been studied by many researchers. Here, we try to explain its biological behavior based on our recent autopsy information and review of literature. METHODS: In this study, patients with a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) result were considered eligible for enrollment. Histopathological examinations were done on 13 people who were hospitalized in Afzalipour hospital, Kerman, Iran. Clinical and laboratory data were reviewed. Tissue examination was done by light microscopy, immunohistochemistry and electron microscopy. RESULTS: The most frequent co-morbidity in the patients was cardiovascular disease. The common initial symptoms of COVID-19 infection were dyspnea and cough. In all cases, the number of white blood cells was higher than the normal range. Common histopathological findings were variable degrees of vasculitis as degenerative to necrotic changes of endothelium and trafficking of inflammatory cells in the vessel wall with fibrinoid necrosis. Tissue damage included interstitial acute inflammatory cells reaction with degenerative to necrotic changes of the parenchymal cells. CD34 and Factor VIII immunohistochemistry staining showed endothelial cell degeneration to necrosis at the vessel wall and infiltration by inflammatory cells. Electron microscopic features confirmed the degenerative damages in the endothelial cells. CONCLUSION: Our histopathological studies suggest that the main focus of the viral damage is the endothelial cells (endotheliopathica) in involved organs. Also, our findings suggest that degeneration of leukocytes occurs at the site of inflammation and release of cytokines (leukocytoclastica) resulting in a cytokine storm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".