Angiotensin-Converting Enzyme 2 (ACE2), Transmembrane Peptidase Serine 2 (TMPRSS2), and Furin Expression Increases in the Lungs of Patients with Idiopathic Pulmonary Fibrosis (IPF) and Lymphangioleiomyomatosis (LAM): Implications for SARS-CoV-2 (COVID-19) Infections
Bibliographic record
Abstract
We previously reported higher ACE2 levels in smokers and patients with COPD. The current study investigates if patients with interstitial lung diseases (ILDs) such as IPF and LAM have elevated ACE2, TMPRSS2, and Furin levels, increasing their risk for SARS-CoV-2 infection and development of COVID-19. Surgically resected lung tissue from IPF, LAM patients, and healthy controls (HC) was immunostained for ACE2, TMPRSS2, and Furin. Percentage ACE2, TMPRSS2, and Furin expression was measured in small airway epithelium (SAE) and alveolar areas using computer-assisted Image-Pro Plus 7.0 software. IPF and LAM tissue was also immunostained for myofibroblast marker α-smooth muscle actin (α-SMA) and growth factor transforming growth factor beta1 (TGF-β1). Compared to HC, ACE2, TMPRSS2 and Furin expression were significantly upregulated in the SAE of IPF (p < 0.01) and LAM (p < 0.001) patients, and in the alveolar areas of IPF (p < 0.001) and LAM (p < 0.01). There was a significant positive correlation between smoking history and ACE2 expression in the IPF cohort for SAE (r = 0.812, p < 0.05) and alveolar areas (r = 0.941, p < 0.01). This, to our knowledge, is the first study to compare ACE2, TMPRSS2, and Furin expression in patients with IPF and LAM compared to HC. Descriptive images show that α-SMA and TGF-β1 increase in the IPF and LAM tissue. Our data suggests that patients with ILDs are at a higher risk of developing severe COVID-19 infection and post-COVID-19 interstitial pulmonary fibrosis. Growth factors secreted by the myofibroblasts, and surrounding tissue could further affect COVID-19 adhesion proteins/cofactors and post-COVID-19 interstitial pulmonary fibrosis. Smoking seems to be the major driving factor in patients with IPF.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".