Platelet Activating Factor Receptor and Intercellular Adhesion Molecule–1 Expression Increases in the Small Airway Epithelium and Parenchyma of Patients with Idiopathic Pulmonary Fibrosis: Implications for Microbial Pathogenesis
Bibliographic record
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is an irreversible lung fibrotic disorder of unknown cause. It has been reported that bacterial and viral co-infections exacerbate disease pathogenesis. These pathogens use adhesion molecules such as platelet activating factor receptor (PAFR) and intercellular adhesion molecule-1 (ICAM–1) to gain cellular entry, causing infections. Methods: Immunohistochemical staining was carried out for lung resections from IPF patients (n = 11) and normal controls (n = 12). The quantification of PAFR and ICAM–1 expression is presented as a percentage in the small airway epithelium. Also, type 2 pneumocytes and alveolar macrophages were counted as cells per mm2 of the parenchymal area and presented as a percentage. All image analysis was done using Image Pro Plus 7.0 software. Results: PAFR expression significantly increased in the small airway epithelium (p < 0.0001), type 2 pneumocytes (p < 0.0001) and alveolar macrophages (p < 0.0001) compared to normal controls. Similar trend was observed for ICAM–1 expression in the small airway epithelium (p < 0.0001), type 2 pneumocytes (p < 0.0001) and alveolar macrophages (p < 0.0001) compared to normal controls. Furthermore, the proportion of positively expressed type 2 pneumocytes and alveolar macrophages was higher in IPF than in normal control. Conclusions: This is the first study to show PAFR and ICAM–1 expression in small airway epithelium, type 2 pneumocytes and alveolar macrophages in IPF. These findings could help intervene microbial impact and facilitate management of disease pathogenesis.
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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 | 0.001 |
| 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) | 0.002 | 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".