The Processes and Mechanisms of Cardiac and Pulmonary Fibrosis
Bibliographic record
Abstract
Fibrosis is the formation of fibrous connective tissue in response to injury. It is characterized by the accumulation of extracellular matrix components, particularly collagen, at the site of injury. Fibrosis is an adaptive response that is a vital component of wound healing and tissue repair. However, its continued activation is highly detrimental and a common final pathway of numerous disease states including cardiovascular and respiratory disease. Worldwide, fibrotic diseases cause over 800,000 deaths per year, accounting for ~45% of total deaths. With an aging population, the incidence of fibrotic disease and subsequently the number of fibrosis-related deaths will rise further. Although, fibrosis is a well-recognized cause of morbidity and mortality in a range of disease states, there are currently no viable therapies to reverse the effects of chronic fibrosis. Numerous predisposing factors contribute to the development of fibrosis. Biological aging in particular, interferes with repair of damaged tissue, accelerating the transition to pathological remodeling, rather than a process of resolution and regeneration. When fibrosis progresses in an uncontrolled manner, it results in the irreversible stiffening of the affected tissue, which can lead to organ malfunction and death. Further investigation into the mechanisms of fibrosis is necessary to elucidate novel, much needed, therapeutic targets. Fibrosis of the heart and lung make up a significant proportion of fibrosis-related deaths. It has long been established that the heart and lung are functionally and geographically linked when it comes to health and disease, and thus exploring the processes and mechanisms that contribute to fibrosis of each organ, the focus of this review, may help to highlight potential avenues of therapeutic investigation.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".