PAFR and ICAM-1 expression increases in patients with IPF: implications for microbial pathogenesis
Bibliographic record
Abstract
Introduction: Idiopathic pulmonary fibrosis (IPF) is an irreversible lung disorder of unknown cause. Bacterial and viral co-infections exacerbate pathogenesis, using adhesion molecules like platelet activating factor receptor (PAFR) and intercellular adhesion molecule 1 (ICAM-1) for cellular entry, causing infections. This study compares PAFR and ICAM-1 expression in IPF patients' small airways and lung parenchyma with normal lungs and COVID-19-related marker (ACE2) expression, exploring if IPF patients are more susceptible to infections. Methods: Surgically resected lung tissue from 11 IPF patients and 12 normal controls (NC) were immunohistochemically stained for PAFR, ICAM-1 and ACE2. Positive expression percentages of each marker in the small airway epithelium and the lung parenchymal area were assessed. All image analysis was done using Image Pro Plus 7 software. Results: Compared to NC, IPF patients had significantly higher levels of PAFR expression in the small airway epithelium (p<0.0001), type 2 pneumocytes (p<0.05) and alveolar macrophages (p<0.05). Similarly, elevated ICAM-1 levels were observed in IPF patients in the epithelium (p<0.0001), type 2 pneumocytes (p<0.0001) and alveolar macrophages (p<0.0001) compared to NC. Furthermore, ACE2 expression was significantly increased in the epithelium (p<0.01) and in lung parenchyma (p<0.001) of IPF patients compared to NC. ICAM-1 surpassed ACE2 and PAFR expression in IPF. Conclusion: This study is the first evaluation of PAFR and ICAM-1 expression in small airway epithelium, type 2 pneumonocytes and alveolar macrophages in IPF, and compared to ACE2. These insights could help mitigate microbial impact and manage disease progression.
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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 | 0.000 | 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) | 0.002 | 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".