Activin A in Inflammation, Tissue Repair, and Fibrosis: Possible Role as Inflammatory and Fibrotic Mediator of Uterine Fibroid Development and Growth
Bibliographic record
Abstract
Abstract The growth factor activin A belongs to the transforming growth factor-β superfamily and was initially isolated as an inducer of follicle-stimulating hormone secretion. Activin A was later found to play roles in cell proliferation, differentiation, apoptosis, and metabolism. More recently, activin A has also been recognized as a novel player in mediating inflammation, immunity, wound repair, and fibrosis. Elevated levels of activin A during inflammation are responsible for the increased production of extracellular matrix in different pathological conditions, including fibroids. Our group has demonstrated a profibrotic role of activin A in leiomyoma growth. Uterine leiomyoma can be considered as a fibrotic disorder that initiates from myometrial smooth muscle layer of uterus in reproductive-age women and that is driven by a strong inflammatory component. In fertile women, transient inflammation is a physiological and essential process during menstruation, ovulation, and parturition. However, tissue injury from extravasated menstrual blood and/or an altered response to harmful stimuli, such as pathogens, damaged cells, or irritants, can establish chronic inflammation in the uterus, ultimately leading to dysregulated tissue repair. Myofibroblasts are key cells in normal repair and the chronic tissue remodeling characteristic for fibrosis and uterine leiomyoma. In this review, we discuss the role of activin A in inflammation, tissue repair, and fibrosis and we elaborate the hypothesis that it plays a central role in myofibroblast activation and leiomyoma development and growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".