Objective:To observe the changes of coagulation and fibrinolysis function in human umbilical cord vein endothelial cells (HUVEC) induced by recombinant human tumor necrosis factor-α (rhT-NF-α) and the effect of Naot...Objective:To observe the changes of coagulation and fibrinolysis function in human umbilical cord vein endothelial cells (HUVEC) induced by recombinant human tumor necrosis factor-α (rhT-NF-α) and the effect of Naotaifang extract (脑泰方, NTE) on it. Methods: Cultured HUVEC is randomly divided into six groups:Control group, NTE control group (only 2 g/L NTE), rhTNF-α group (100ug/ L rhTNF-α), and low-dosage, middle-dosage and high-dosage NTE group (100ug/L rhTNF-α and 0. 67 g/L, 2 g/L, 6 g/L NTE). The coagulation activity of frozen-dissolved HUVEC, von Willebrand factor (vWF) content in the conditioned medium and tissue-type plasminogen activator (tPA) and plasminogen activator inhibitor (PAD activity were to be detected after 24 hrs. Results: Compared with the control group, PAI activity were enhanced, vWF release markedly increased in conditioned medium of TNF-α group (P<0. 01) and the frozen-dissolved HUVEC markedly shortens the rabbit plasma prothrombin time, and the above changes could be significantly inhibited by the 3 dosages of NTE (P<0. 05, P<0. 01). Conclusion:NTE is effective in inhibiting the coagulation activity of the HUVEC non-stimulated or stimulated by rhTNF-a to enhance the vWF release, and to adjust fibrinolytic function, and mainly to inhibit the PAI activity.展开更多
Objective: Inflammation plays a critical role in atherosclerosis, and this inflammatory reaction is being intensively studied. Shenlian Extracts (参莲提取物), an active ingredient of Chinese medicinal herbs, is believ...Objective: Inflammation plays a critical role in atherosclerosis, and this inflammatory reaction is being intensively studied. Shenlian Extracts (参莲提取物), an active ingredient of Chinese medicinal herbs, is believed to have multiple therapeutic and preventive effects against human vascular diseases, including atherosclerosis. Our work investigated whether Shenlian Extracts serves as an anti-inflammatory agent during atherogenesis. Methods: We established a model of atherosclerosis in rabbits using balloon angioplasty and a high cholesterol diet. The effects of Shenlian Extracts on vessel structure and inflammation were assessed by hematoxylin-eosin staining of the femoral artery, measurement of inflammation-related factors in serum or vascular tissue, and radioimmunoassay. Enzyme linked immunosorbent assays (ELISA), flow cytometry and western blots were also performed. Results: We show that oral pre-treatment with Shenlian Extracts suppressed the pathological changes associated with atherosclerosis and that graded doses of Shenlian Extracts reduced total serum levels of cholesterol (90, 180 and 360 mg/kg), triglyceride (180 and 360 mg/kg), and LDL-c (90, 180 mg/kg). Various doses of Shenlian Extracts reduced serum content of TNF-α (180 and 360 mg/kg), CRP (90, 180 and 360 mg/kg) and IL-8 (360 mg/kg) (P<0.05), but led to no significant changes in IL-1β levels. Treatment with Shenlian Extracts also significantly reduced VCAM-1 levels (90 and 360 mg/kg) and IGF-1 levels (90 and 180 mg/kg) in vascular tissue but had no significant effect on ICAM-1 and MCP-1 levels. Finally, Shenlian Extracts significantly reduced the abnormal expression of CD18 in monocytes in a dose-dependent manner. Conclusion: These results suggest that Shenlian Extracts may play a direct role in preventing and treating atherogenesis by inhibiting the inflammatory reaction, providing insights into the possible mechanism underlying the anti-atherosclerotic actions of Shenlian Extracts.展开更多
As artificial intelligence(AI)technology evolves,data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life(RUL).Indeed,accurate RUL prediction is challenging,primarily ...As artificial intelligence(AI)technology evolves,data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life(RUL).Indeed,accurate RUL prediction is challenging,primarily because of the complex nature of the work and dynamic shifts in model parameters.To address these challenges,this article comprehensively explores five significant publicly accessible lithium-ion battery datasets,encompassing diverse usage conditions and battery types,offering researchers a rich repository of experimental data.In particular,we not only provide detailed information and access addresses for each dataset,but also present,four innovative methods for battery aging health factor extraction.These methods,based on advanced AI techniques,are able to effectively identify and quantify key indicators of battery performance degradation,thereby enhancing the precision and dependability of RUL prediction.Additionally,the article identifies major challenges faced by current predictive techniques,including data quality,model generalization capabilities,and computational cost,highlighting the need for research focused on dataset diversity,multiple algorithm fusion,and hybrid physicaldata-driven models to enhance prediction accuracy.We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.展开更多
基金The item is supported by Natural Science Foundation of Hunan Province (No. 98JJY 2027)Young-middle Aged Science and Technological Fund of Hunan Province (NO.00JZYZ145)
文摘Objective:To observe the changes of coagulation and fibrinolysis function in human umbilical cord vein endothelial cells (HUVEC) induced by recombinant human tumor necrosis factor-α (rhT-NF-α) and the effect of Naotaifang extract (脑泰方, NTE) on it. Methods: Cultured HUVEC is randomly divided into six groups:Control group, NTE control group (only 2 g/L NTE), rhTNF-α group (100ug/ L rhTNF-α), and low-dosage, middle-dosage and high-dosage NTE group (100ug/L rhTNF-α and 0. 67 g/L, 2 g/L, 6 g/L NTE). The coagulation activity of frozen-dissolved HUVEC, von Willebrand factor (vWF) content in the conditioned medium and tissue-type plasminogen activator (tPA) and plasminogen activator inhibitor (PAD activity were to be detected after 24 hrs. Results: Compared with the control group, PAI activity were enhanced, vWF release markedly increased in conditioned medium of TNF-α group (P<0. 01) and the frozen-dissolved HUVEC markedly shortens the rabbit plasma prothrombin time, and the above changes could be significantly inhibited by the 3 dosages of NTE (P<0. 05, P<0. 01). Conclusion:NTE is effective in inhibiting the coagulation activity of the HUVEC non-stimulated or stimulated by rhTNF-a to enhance the vWF release, and to adjust fibrinolytic function, and mainly to inhibit the PAI activity.
基金supported by grants of the National Natural Science Foundation of China (30973901)the Major Program for Technique Development Research of Novel Traditional Chinese Medicine Production (2009 ZX093 01-005- 2-4)+1 种基金the Autonomous Program of China Academy of Chinese Medical Sciences (ZZ20090207)the International S&T Cooperation Project (S2011ZR0193)
文摘Objective: Inflammation plays a critical role in atherosclerosis, and this inflammatory reaction is being intensively studied. Shenlian Extracts (参莲提取物), an active ingredient of Chinese medicinal herbs, is believed to have multiple therapeutic and preventive effects against human vascular diseases, including atherosclerosis. Our work investigated whether Shenlian Extracts serves as an anti-inflammatory agent during atherogenesis. Methods: We established a model of atherosclerosis in rabbits using balloon angioplasty and a high cholesterol diet. The effects of Shenlian Extracts on vessel structure and inflammation were assessed by hematoxylin-eosin staining of the femoral artery, measurement of inflammation-related factors in serum or vascular tissue, and radioimmunoassay. Enzyme linked immunosorbent assays (ELISA), flow cytometry and western blots were also performed. Results: We show that oral pre-treatment with Shenlian Extracts suppressed the pathological changes associated with atherosclerosis and that graded doses of Shenlian Extracts reduced total serum levels of cholesterol (90, 180 and 360 mg/kg), triglyceride (180 and 360 mg/kg), and LDL-c (90, 180 mg/kg). Various doses of Shenlian Extracts reduced serum content of TNF-α (180 and 360 mg/kg), CRP (90, 180 and 360 mg/kg) and IL-8 (360 mg/kg) (P<0.05), but led to no significant changes in IL-1β levels. Treatment with Shenlian Extracts also significantly reduced VCAM-1 levels (90 and 360 mg/kg) and IGF-1 levels (90 and 180 mg/kg) in vascular tissue but had no significant effect on ICAM-1 and MCP-1 levels. Finally, Shenlian Extracts significantly reduced the abnormal expression of CD18 in monocytes in a dose-dependent manner. Conclusion: These results suggest that Shenlian Extracts may play a direct role in preventing and treating atherogenesis by inhibiting the inflammatory reaction, providing insights into the possible mechanism underlying the anti-atherosclerotic actions of Shenlian Extracts.
基金supported by the National Natural Science Foundation of China(No.22278127)the Fundamental Research Funds for the Central Universities(No.2022ZFJH004)the Shanghai Pilot Program for Basic Research(22T01400100-18).
文摘As artificial intelligence(AI)technology evolves,data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life(RUL).Indeed,accurate RUL prediction is challenging,primarily because of the complex nature of the work and dynamic shifts in model parameters.To address these challenges,this article comprehensively explores five significant publicly accessible lithium-ion battery datasets,encompassing diverse usage conditions and battery types,offering researchers a rich repository of experimental data.In particular,we not only provide detailed information and access addresses for each dataset,but also present,four innovative methods for battery aging health factor extraction.These methods,based on advanced AI techniques,are able to effectively identify and quantify key indicators of battery performance degradation,thereby enhancing the precision and dependability of RUL prediction.Additionally,the article identifies major challenges faced by current predictive techniques,including data quality,model generalization capabilities,and computational cost,highlighting the need for research focused on dataset diversity,multiple algorithm fusion,and hybrid physicaldata-driven models to enhance prediction accuracy.We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.
