All-<i>trans</i>retinoic acid stimulates gene expression of the cardioprotective natriuretic peptide system and prevents fibrosis and apoptosis in cardiomyocytes of obese<i>ob/ob</i>mice
Bibliographic record
Abstract
In hypertensive rodents, retinoic acid (RA) prevents adverse cardiac remodelling and improves myocardial infarction outcome, but its role in obesity-related changes of cardiac tissue are unclear. We hypothesized that all-trans RA (ATRA) treatment will improve the cardioprotective oxytocin-natriuretic peptides (OT-NP) system, preventing apoptosis and collagen accumulation in hearts of ob/ob mice, a mouse model of obesity and insulin resistance. Female 9-week-old B6.V-Lep/J ob/ob mice (n = 16) were divided into 2 groups: 1 group (n = 8) treated with 100 μg of ATRA dissolved in 100 μL of corn oil (vehicle) delivered daily (∼2 μg·g body weight(-1)·day(-1)) by stomach intubation for 16 days, and 1 group (n = 8) that received the vehicle alone. A group of nonobese littermate mice (n = 9) served as controls. Ob/ob mice exhibited obesity, hyperglycaemia, and downregulation of the cardiac OT-NP system, including the mRNA for the transcription factor GATA4, OT receptor and brain NP, and the protein expression for endothelial nitric oxide synthase. Hearts from ob/ob mice also demonstrated increased apoptosis and collagen accumulation. ATRA treatment induced weight loss and decreased adipocytes diameter in the visceral fat, thus reducing visceral obesity, which is associated with a high risk for cardiovascular disease. RA treatment was associated with a reduction in hyperglycemia and a normalization of the OT-NP system's expression in the hearts of ob/ob mice. Furthermore, ATRA treatment prevented apoptosis and collagen accumulation in hearts of ob/ob mice. The present study indicates that ATRA treatment was effective in restoring the cardioprotective OT-NP system and in preventing abnormal cardiac remodelling in the ob/ob mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".