Regulation of Overnutrition-Induced Cardiac Inflammatory Mechanisms by nebivolol
Bibliographic record
Abstract
BACKGROUND: Unlike conventional β-blockers, nebivolol, a third-generation β-adrenergic receptor blocker with vasodilator properties, promotes insulin sensitivity. Objective: The objective of this study was to determine whether nebivolol regulates overnutrition-induced activation of cardiac nutrient sensor kinases and inflammatory signaling. METHODS: Young Zucker obese (ZO) rats, a rodent model for overnutrition, and age-matched Zucker lean rats were treated with nebivolol (10 mg/kg/day; 21 days) and cardiac function was monitored by echocardiography and pressure volume loop analysis. Activation status of nutrient sensor serine/threonine kinases mammalian target for rapamycin (mTOR), and p70 S6kinase (S6K1) and S6K1-substrate RPS6, inflammatory marker Janus kinase 2 (Jak2) and its substrate STAT1, and energy sensor AMP-dependent kinase (AMPK) were monitored by determining phosphorylation status of pSer(2448) of mTOR, pThr(389) of S6K1, pSer(235/236) of RPS6, pTyr(1007/1008) of Jak2, pTyr(701) of STAT1, and pThr(172) of AMPK, respectively. RESULTS: Nebivolol reduced weight and improved cardiac function of ZO rats as shown by improvements in the myocardial performance index and a decrease in the diastolic parameter tau (τ), the time constant of isovolumic relaxation. Nebivolol also attenuated excessive activation of the nutrient sensor kinases mTOR and S6K1 and their substrate RPS6 as well as the inflammatory marker Jak2 and substrate STAT1 in ZO myocardium (p < 0.05). Moreover, nebivolol reversed suppression of the energy sensor kinase AMPK in ZO hearts (p < 0.05). CONCLUSION: We report for the first time that nebivolol regulates overnutrition-induced activation of cardiac mTOR and Jak/STAT signaling and reverses suppression of AMPK. Since it also suppresses weight gain, nebivolol appears effective in the treatment of overnutrition-related cardiac inflammation and diastolic dysfunction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".