Bibliographic record
Abstract
PURPOSE OF REVIEW: This review highlights recent discoveries and advances that have been made in understanding the role of the TGFβ superfamily members activins, and in particular, activin A (ActA), in renal disease. RECENT FINDINGS: A deleterious role for ActA in renal disease and its complications has begun to emerge. We summarize data supporting an important contribution of ActA to kidney fibrosis and inflammation of varying causes, as well as its role in the development of a particular bone mineral disorder seen in chronic kidney disease (CKD) called mineral bone disorder (MBD), including vascular calcification. Finally, we discuss ActA in the context of anemia associated with chronic kidney disease and review potential approaches to treatment based on ActA blockade. SUMMARY: ActA is an important contributor to the pathogenesis of acute and chronic kidney disease of varying causes. Preclinical studies show that ActA inhibition, through various approaches, is protective in rodent models of kidney disease. The potential adverse effects of some of these approaches can be attributed to their targeting of other TGFβ family ligands. Further preclinical and clinical investigations testing the therapeutic efficacy of more selective ActA inhibition on the progression of acute and chronic kidney disease and its impact on bone-mineral disorder would more definitively establish its role in renal disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".