Sevoflurane-mediated modulation of oxidative myocardial injury.

Introduction: Volatile anesthetics offer protection when administered throughout an ischemic injury. We examined how volatile anesthetics modulate the cardiac myocytic injury associated with hydrogen peroxide. Methods: Forty-eight Long-Evans rats were divided into four groups depending on the treatment: none (CONT), Glibenclamide (GLB); Sevoflurane (SEV); or GLB+SEV. Each group was further divided into two, one of which was exposed to hydrogen peroxide (H2O2). Oral GLB was administered 48 hours before myocardial isolation. All rats were anesthetized by intraperitoneal injection of Ketamine, and the hearts were harvested after heparinization. Cardiomyocytes were isolated using a combination of mechanical mincing and enzymatic digestion. After isolation, the aliquots of cells were exposed to H2O2 and FeSO4 for 30 minutes. The cell suspensions were then bubbled for 10 minutes with 100% oxygen and 1.5% SEV if appropriate. Apoptosis was detected by fluorescein-bound annexin-V (ANX-V), necrosis by propidium iodide, and ELISA assessed caspase-3 activity in all groups. Results: There was an increase in apoptosis, necrosis, and caspase-3 activity in the cells following exposure to hydrogen peroxide. SEV reduced the rate of cell necrosis and apoptosis. Pretreatment with GLB did not alter the effects of SEV. Similarly, caspase-3 activity did not change with GLB, although SEV administration reduced this enzymatic activity in response to hydrogen peroxide. Conclusion: In this oxidant injury model, we demonstrated that incubating isolated cardiomyocytes with SEV profoundly diminished H2O2-induced apoptotic and necrotic cells compared to their CONTs. These results support the hypothesis that KATP channels are not the sole mediators associated with anesthetic preconditioning.

Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures.

Recent advancements in deep learning have led to widespread applications of its algorithms to synthetic planning and reaction predictions in the field of chemistry. One major area, known as supervised learning, is being explored for predicting certain properties such as reaction yields and types. Many chemical descriptors known as fingerprints are being explored as potential candidates for reaction properties prediction. However, there are few studies that describe the permutational invariance of chemical fingerprints, which are concatenated at some stage before being fed to deep learning architecture. In this work, we show that by utilizing permutational invariance, we consistently see improved results in terms of accuracy relative to previously published studies. Furthermore, we are able to accurately predict hydrogen peroxide loss with our own dataset, which consists of more than 20 ingredients in each chemical formulation.