Objective To test the ability of isoflurane-induced preconditioning against oxygen and glucose dep- rivation （OGD） injury in vitro. Methods Rat hippocampal slices were exposed to 1 volume percentage （vol%）, 2vo1%...Objective To test the ability of isoflurane-induced preconditioning against oxygen and glucose dep- rivation （OGD） injury in vitro. Methods Rat hippocampal slices were exposed to 1 volume percentage （vol%）, 2vo1% or 3vo1% isoflurane respectively for 20 minutes under normoxic conditions （95% O2/5% CO2） once or twice （12 slices in each group） before OGD, with 15-minute washout after each exposure. During OGD experiments, hippocampus slices were bathed with artificial cerebrospinal fluid （ACSF） lacking glucose and perfused with 95% N2 and 5% CO2 for 14 minutes, followed by a 30-minute reperfusion in normal ACSF. The CA1 population spike （PS） was measured and used to quantify the degree of neuronal function recovery after OGD. To assess the role of mitogen-activated protein kinases （MAPKs） in isoflurane preconditioning, U0126, an inhibitor of extracellular signal-regulated protein kinase （ERK1/2）, and SB203580, an inhibitor of p38 MAPK, were used before two periods of 3vol% isoflurane exposure. Results The degree of neuronal function recovery of hippocampal slices exposed to 1 vol%, 2vol%, or 3vol% isoflurane once was 41.88%±9.23%, 55.05%±11.02%, or 63.18%±10.82% respectively. Moreover, neuronal function recovery of hippocampal slices exposed to 1 vol%, 2vo1%, or 3vo1% isoflurane twice was 53.75%±12.04%, 63.50%±11.06%, or 76.25%±12.25%, respectively. Isoflurane preconditioning increased the neuronal function recovery in a dose-dependent manner. U0126 blocked the preconditioning induced by dual exposure to 3vo1% isoflurane （6.13%±1.56%, P〈0.01） and ERK1/2 activities. Conclusions Isoflurane is capable of inducing preconditioning in hippocampal slices in vitro in a dose-dependent manner, and dual exposure to isoflurane with a lower concentration is more effective in triggering preconditioning than a single exposure. Isoflurane-induced neuroprotection might be involved with ERK 1/2 activities.展开更多
Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formula...Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling.Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels.Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.展开更多
基金Supported by Foundation of Shihezi University of Xinjiang Province (RCZX200688)
文摘Objective To test the ability of isoflurane-induced preconditioning against oxygen and glucose dep- rivation （OGD） injury in vitro. Methods Rat hippocampal slices were exposed to 1 volume percentage （vol%）, 2vo1% or 3vo1% isoflurane respectively for 20 minutes under normoxic conditions （95% O2/5% CO2） once or twice （12 slices in each group） before OGD, with 15-minute washout after each exposure. During OGD experiments, hippocampus slices were bathed with artificial cerebrospinal fluid （ACSF） lacking glucose and perfused with 95% N2 and 5% CO2 for 14 minutes, followed by a 30-minute reperfusion in normal ACSF. The CA1 population spike （PS） was measured and used to quantify the degree of neuronal function recovery after OGD. To assess the role of mitogen-activated protein kinases （MAPKs） in isoflurane preconditioning, U0126, an inhibitor of extracellular signal-regulated protein kinase （ERK1/2）, and SB203580, an inhibitor of p38 MAPK, were used before two periods of 3vol% isoflurane exposure. Results The degree of neuronal function recovery of hippocampal slices exposed to 1 vol%, 2vol%, or 3vol% isoflurane once was 41.88%±9.23%, 55.05%±11.02%, or 63.18%±10.82% respectively. Moreover, neuronal function recovery of hippocampal slices exposed to 1 vol%, 2vo1%, or 3vo1% isoflurane twice was 53.75%±12.04%, 63.50%±11.06%, or 76.25%±12.25%, respectively. Isoflurane preconditioning increased the neuronal function recovery in a dose-dependent manner. U0126 blocked the preconditioning induced by dual exposure to 3vo1% isoflurane （6.13%±1.56%, P〈0.01） and ERK1/2 activities. Conclusions Isoflurane is capable of inducing preconditioning in hippocampal slices in vitro in a dose-dependent manner, and dual exposure to isoflurane with a lower concentration is more effective in triggering preconditioning than a single exposure. Isoflurane-induced neuroprotection might be involved with ERK 1/2 activities.
基金supported by the National Key Research&Develop Plan of China under Grant Nos.2017YFB1400700 and 2018YFB1004401the National Natural Science Foundation of China under Grant Nos.61732006,61702522,61772536,61772537,62076245,and 62072460Beijing Natural Science Foundation under Grant No.4212022。
文摘Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth label.This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling.Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels.Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.
