Adipose tissue deposition in lungs of patients with IPF
Bibliographic record
Abstract
Introduction/Aim: Idiopathic pulmonary fibrosis (IPF) is a rapidly progressing and irreversible fibrotic disease. Obesity has been reported to contribute to inflammation and fibrotic changes in chronic lung disease, though its role in IPF remains under investigation. In this study, we present novel findings of adipose tissue deposition in the lungs of patients with IPF and its association with respiratory parameters. Methods: We used surgically resected lung tissue from 23 IPF patients compared to 21 normal controls (NC), using hematoxylin and eosin (H&E) and Sudan Black B with fast red staining methods, and then quantified the adipose tissue area and adipocyte cells in the lung parenchymal area using Image-Pro plus 7.0 software. Results: There was no difference between the two groups for body mass index (BMI). However, compared to NC (percentage deposition, median, range: 2.79, 0 – 10.63; adipocytes median, range: 13.21, 0 – 31.27), the percentage of adipose tissue (median, range: 21.53, 0 – 58.63, p< 0.0001) and the number of adipocyte cells (cells per mm2) (median, range: 36.57, 0 – 94.81, p< 0.0001) in patients with IPF was significantly higher, as well as had a positive correlation with BMI (p≤ 0.05). In addition, we found statistically significant negative correlations between lung function parameters FEV1 and adipose tissue percentage (r’= -0.4558, p= 0.0446) and the number of adipocytes (r’= -0.4505, p= 0.0466) in IPF, respectively. Conclusion: Our study provides preliminary data indicating a highly significant deposition of adipose tissue in the lung from IPF patients compared to normal controls. Excessive deposition of adipocyte tissue in IPF lung may promote inflammation and fibrosis, but more work is needed.
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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".