ACTIVIN B IN DIABETIC KIDNEY DISEASE
Bibliographic record
Abstract
DKD is the leading cause of kidney failure in Canada and its patients suffer the highest morbidity and mortality rates of any kidney failure patient group. Current interventions including strict glycemic control only delay DKD. Thus, there is a major need to identify new therapeutic targets. High glucose (HG) is identified as a major pathogenic factor, inducing the release of growth factors leading to kidney fibrosis. Although treatments have been developed to target these factors, their effectiveness is accompanied by adverse effects due to the lack of specificity. Recently, activins have been suggested to have a prominent role in promoting renal fibrosis and developing a specific anti-activin therapy can avoid potential side effects. Although there is evidence supporting an important role for activin A (ActA) in the induction of fibrosis in DKD, whether ActB also contributes is unknown. In this study, we aim to determine the potential contribution of ActB to promoting fibrosis. Our results show that ActA and ActB are upregulated in rodent and human DKD. We show that hyperglycemia leads to the secretion of ActA and ActB by mesangial cells (MC), whereas only ActB is secreted by renal fibroblasts (RF). Similar to HG, treatment with ActA or ActB leads to Smad2/3 activation and upregulation of extracellular matrix proteins, whereas specific inhibition of either ActA or ActB attenuates these effects. We show that ActA and ActB regulate HG-induced activation of MRTF-A/SRF in MC, leading to an activated phenotype characterized by increased α-SMA expression and ECM production. Lastly, we confirm the specificity and functionality of the activin propeptides in vitro, providing evidence for their effectiveness in vivo. This study will help further our knowledge of the role activins in DKD, potentially providing an alternative therapy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".