Abstract 024: MicroRNAs and Anesthetic Cardioprotection in Diabetes
Bibliographic record
Abstract
Introduction: MicroRNAs are endogenous small RNA molecules that regulate a wide range of cellular functions primarily through reduction of target protein expression. Several microRNAs have been shown to play important roles in cardiac injury, and also contribute to the development of diabetic complications and cardiac preconditioning. We utilized a model of the patient-specific induced pluripotent stem cells (iPSCs) differentiated into the cardiac lineage in order to delineate the environmental and cellular mechanisms responsible for overturning anesthetic cardioprotection in diabetes. We hypothesized that miR-21 contributes to cardioprotection conferred by anesthetics in human cardiomyocytes and that diabetic conditions compromise this protection in part via suppression of miR-21. Methods: We have developed and validated a clinically relevant model of cardioprotection using human cardiomyocytes differentiated from the iPSCs derived from non-diabetic individuals (N-CM) and patients with type 2 diabetes mellitus (T2-CM). Results: Our results indicate that cardiomyocytes derived from type 2 diabetes-specific stem cells recapitulate the phenotypic findings from type 2 diabetic patients. For instance, T2-CM exhibited a suppression of protein kinase B (Akt) and activation of glycogen synthase kinase-3β, compared to N-CM; indicating that this pathway is compromised in cardiomyocytes derived from diabetic individuals. In addition, we examined whether isoflurane could delay oxidative stress-induced mitochondrial permeability transition pore (mPTP) opening in T2-CM and found that the effects of isoflurane were significantly attenuated as compared to N-CM. Finally, isoflurane increased miR-21 abundance in N-CM, but not in T2-CM. Summary: Diabetes and hyperglycemia substantially increase perioperative cardiovascular risk, with few mitigating strategies. Our data indicate an important role of miR-21 in isoflurane-induced cardioprotection and its impairment by diabetic conditions that may suggest new therapeutic targets for reducing perioperative cardiovascular morbidity and mortality in high-risk patients.
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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 | 0.001 | 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".