Lung Fibrosis: Drug Screening and Disease Biomarker Identification with a Lung Slice Culture Model and Subtracted cDNA Library
Bibliographic record
Abstract
Pulmonary fibrosis is a progressive and irreversible disorder with no appropriate cure. A practical and effective experimental model that recapitulates the disease will greatly benefit the research community and, ultimately, patients. In this study, we tested the lung slice culture (LSC) system for its potential use in drug screening and disease biomarker identification. Fibrosis was induced by treating rat lung slices with 1ng/ml TGF-β1 and 2.5μM CdCl2, quantified by measuring the content of hydroxyproline, and confirmed by detecting the expression of collagen type III alpha 1 (Col3α1) and connective tissue growth factor (CTGF) genes. The anti-fibrotic effects of pirfenidone, spironolactone and eplerenone were assessed by their capability to reduce hydroxyproline content. A subtractive hybridisation technique was used to create two cDNA libraries (subtracted and unsubtracted) from lung slices. The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was employed to assess the subtraction efficiency of the subtracted cDNA library. Clones from the two libraries were sequenced and the genes were identified by performing a BLAST search on the NCBI GenBank database. Furthermore, the relevance of the genes to fibrosis formation was verified. The results presented here show that fibrosis was effectively induced in cultured lung slices, which exhibited significantly elevated levels of hydroxyproline and Col3α1/CTGF gene expression. Several inhibitors have demonstrated their anti-fibrotic effects by significantly reducing hydroxyproline content. The subtracted cDNA library, which was enriched for differentially expressed genes, was used to successfully identify genes associated with fibrosis. Collectively, the results indicate that our LSC system is an effective model for the screening of drug candidates and for disease biomarker identification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".