Alamandine via MrgD receptor attenuates pulmonary fibrosis via NOX4 and autophagy pathway
Bibliographic record
Abstract
Alamandine (ALA) and its receptor MrgD were recently identified as components of the renin-angiotensin system, which confer protection against cardio-fibrosis and renal-fibrosis; however, the effects of ALA on pulmonary fibrosis are unknown. This study was designed to serve two goals: (i) to evaluate the ALA/MrgD axis ability in the prevention of angiotensin II (Ang II) – induced pulmonary fibrosis in fibroblasts, and (ii) to determine the effect of ALA in bleomycin (BLM) – treated C57B/6 mice. In vivo experiments revealed that the treatment of C57B/6 mice with ALA prevented BLM-induced fibrosis, and these findings were similar to those reported for pirfenidone. The antifibrosis actions of ALA were mediated via alleviation of oxidative injury and autophagy induction. In addition, in vitro studies revealed that ALA treatment attenuated Ang II–induced α-collagen I, CTGF, and α-SMA production in fibroblast which was blocked by D-Pro7-Ang-(1-7), a MrgD antagonist. This led to alleviation of oxidative injury and induction of autophagy similar to that reported for rapamycin. This study demonstrated that ALA via MrgD receptor reduced pulmonary fibrosis through attenuation of oxidative injury and induction of autophagy.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".