Treatments for fibrosis development and progression: Lessons learned from preclinical models and potential impact on human conditions such as scleroderma, pulmonary fibrosis, hypertrophic scarring and tendinopathies
Bibliographic record
Abstract
Progressive fibrosis of a tissue or organ in response to a damaging insult may result in loss of organ function if the acute response is excessive, or a chronic fibrotic response is initiated due to the persistence of the insult. In the author’s laboratory over the past several years, a number of preclinical models of fibrosis or fibrogenic responses have been characterized for the effectiveness of various treatment approaches to either prevent or impede fibrosis development and progression to identify commonalities and translatable research directions that could provide insights into human diseases. These have mainly included either chemically induced pulmonary fibrosis models or overt physical injury models in rats, pigs and rabbits. Some preliminary studies in human populations have also been undertaken. The interventions evaluated have included fibrinolytic agents and drugs targeting specific cell populations. The results indicate that some approaches lend themselves to modifying fibrotic reactions in some models and not others, while others may have a more generalized impact on fibrogenic responses due to interference with abnormal cell functions in the injury environment.
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 | 0.006 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".