BMF-AS1/BMF Promotes Diabetic Vascular Calcification and Aging both <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Vascular calcification and aging often increase morbidity and mortality in patients with diabetes mellitus (DM); however, the underlying mechanisms are still unknown. In the present study, we found that Bcl-2 modifying factor (BMF) and BMF antisense RNA 1 (BMF-AS1) were significantly increased in high glucose-induced calcified and senescent vascular smooth muscle cells (VSMCs) as well as artery tissues from diabetic mice. Inhibition of BMF-AS1 and BMF reduced the calcification and senescence of VSMCs, whereas overexpression of BMF-AS1 and BMF generates the opposite results. Mechanistic analysis showed that BMF-AS1 interacted with BMF directly and up-regulated BMF at both mRNA and protein levels, but BMF did not affect the expression of BMF-AS1. Moreover, knocking down BMF-AS1 and BMF suppressed the calcification and senescence of VSMCs, and BMF knockout (BMF-/-) diabetic mice presented less vascular calcification and aging compared with wild type diabetic mice. In addition, higher coronary artery calcification scores (CACs) and increased plasma BMF concentration were found in patients with DM, and there was a positive correlation between CACs and plasma BMF concentration. Thus, BMF-AS1/BMF plays a key role in promoting high glucose-induced vascular calcification and aging both in vitro and in vivo. BMF-AS1 and BMF represent potential therapeutic targets in diabetic vascular calcification and aging.
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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) | 0.000 | 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".