Targeting Genes for Treatment in Idiopathic Pulmonary Fibrosis: Challenges and Opportunities, Promises and Pitfalls
Bibliographic record
Abstract
The currently accepted approach to treatment of idiopathic pulmonary fibrosis (IPF) is based on the assumption that it is a chronic inflammatory disease, and most available antiinflammatory drugs target numerous biological processes involving multiple genes, but are not often beneficial. More novel therapeutic strategies take recent findings about the underlying molecular mechanisms of fibrogenesis into account, and ongoing and as yet unpublished clinical trials in IPF aim to block single gene targets believed to play major roles in disease progression. Characterization of the mechanisms involved in the pathogenesis of IPF has largely come from the use of animal disease models in rodents. Most data suggest, from among the different factors, a prominent role for the transforming growth factor (TGF)-beta1 and platelet-derived growth factor pathways. Inflammation is a critical element of the initiation of fibrosis and data indicate that the Smad pathway is a necessary link to fibrosis through TGF-beta and Smad3 signaling, which introduces matrix regulation as a new target for therapeutic intervention. Regardless, gene targeted therapy has numerous pitfalls that have to be addressed before we see a real therapeutic advance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".