Fibrometabolism—An emerging therapeutic frontier in pulmonary fibrosis
Bibliographic record
Abstract
Fibrosis is the final pathological outcome and major cause of morbidity and mortality in many common and chronic inflammatory, immune-mediated, and metabolic diseases. Despite the growing incidence of fibrotic diseases and extensive research efforts, there remains a lack of effective therapies that improve survival. The application of omics technologies has revolutionized our approach to identifying previously unknown therapeutic targets and potential disease biomarkers. The application of metabolomics, in particular, has improved our understanding of disease pathomechanisms and garnered a wave of scientific interest in the role of metabolism in the biology of myofibroblasts, the key effector cells of the fibrogenic response. Emerging evidence suggests that alterations in metabolism not only are a feature of but also may play an influential role in the pathogenesis of fibrosis, most notably in idiopathic pulmonary fibrosis (IPF), the most rapidly progressive and fatal of all fibrotic conditions. This review will detail the role of key metabolic pathways, their alterations in myofibroblasts, and the potential this new knowledge offers for the development of antifibrotic therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".