Endothelial to mesenchymal transition: a precursor to post-COVID-19 interstitial pulmonary fibrosis and vascular obliteration?
Bibliographic record
Abstract
We read with interest the recent editorial by Huertas et al. [1], highlighting the importance of endothelial cell dysfunction in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The authors have made some very fascinating insights, and we would like to take this discussion further, especially emphasising the role of endothelial cells in initiating post-infection pulmonary fibrosis and vascular remodelling. The angiotensin-converting enzyme 2 (ACE2) has been suggested as the primary receptor for mediating SARS-CoV-2 entry into the host cells. Apart from ACE2, the other key players that facilitate SARS-CoV-2 entry, includes transmembrane serine protease 2 (TMPRSS2), furin, sialic acid and the extracellular matrix metalloproteinase inducer (CD147) [1]. In their article, the authors provide compelling comprehensions from a study comparing post mortem lung tissues from patients who died from coronavirus disease 2019 (COVID-19), acute respiratory distress syndrome due to influenza A (H1N1) infection and those from age-matched, uninfected control lungs [1, 2]. A crucial inference was the connections between the significant increase in ACE2 positive endothelial cells and the substantial change in endothelial cell morphology, disruption of intercellular junctions, cell swelling, and the breakdown of the underlying basement membrane, all indicative of vascular structural modification in tune to the process of endothelial to mesenchymal transition (EndMT) [3]. Considering the implications for post-COVID-19 pulmonary fibrosis and vascular destruction seen in this infectious pathology, we believe that the role of EndMT in disease manifestation could be consequential. Endothelial to mesenchymal transition (EndMT) could lead to post-COVID-19 pulmonary fibrosis and vascular remodelling Clifford Craig Foundation Launceston General Hospital, Lung Foundation Australia, The Alfred Hospital Melbourne and James Hogg Lung Registry, The University of British Columbia, Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".