Mitochondrial injury and endoplasmic reticulum(ER)stress are considered to be the key mechanisms of renal ischemia-reperfusion(I/R)injury.Mitochondria are membrane-bound organelles that form close physical contact wit...Mitochondrial injury and endoplasmic reticulum(ER)stress are considered to be the key mechanisms of renal ischemia-reperfusion(I/R)injury.Mitochondria are membrane-bound organelles that form close physical contact with a specific domain of the ER,known as mitochondrial-associated membranes.The close physical contact between them is mainly restrained by ER-mitochondria tethering complexes,which can play an important role in mitochondrial damage,ER stress,lipid homeostasis,and cell death.Several ER-mitochondria tethering complex components are involved in the process of renal I/R injury.A better understanding of the physical and functional interaction between ER and mitochondria is helpful to further clarify the mechanism of renal I/R injury and provide potential therapeutic targets.In this review,we aim to describe the structure of the tethering complex and elucidate its pivotal role in renal I/R injury by summarizing its role in many important mechanisms,such as mitophagy,mitochondrial fission,mitochondrial fusion,apoptosis and necrosis,ER stress,mitochondrial substance transport,and lipid metabolism.展开更多
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases.With the development of data preservation and processing,as well as the advancement of machine learning algorithms,ma...Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases.With the development of data preservation and processing,as well as the advancement of machine learning algorithms,machine learning is expected to make remarkable breakthroughs in nephrology.Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields,including analysis of renal pathological images,diagnosis and prognosis of chronic kidney diseases and acute kidney injury,as well as management of dialysis treatments.However,it is just scratching the surface of the field;at the same time,machine learning andits applications in renal diseases are facing a number of challenges.In this review,we discuss the application status,challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction,detection,and care quality in kidney diseases.展开更多
基金grants from the National Natural Science Foundation of China(Nos.61971441,61671479,and 81804056)the National Key R&D Program of China(No.2016YFC1305500)。
文摘Mitochondrial injury and endoplasmic reticulum(ER)stress are considered to be the key mechanisms of renal ischemia-reperfusion(I/R)injury.Mitochondria are membrane-bound organelles that form close physical contact with a specific domain of the ER,known as mitochondrial-associated membranes.The close physical contact between them is mainly restrained by ER-mitochondria tethering complexes,which can play an important role in mitochondrial damage,ER stress,lipid homeostasis,and cell death.Several ER-mitochondria tethering complex components are involved in the process of renal I/R injury.A better understanding of the physical and functional interaction between ER and mitochondria is helpful to further clarify the mechanism of renal I/R injury and provide potential therapeutic targets.In this review,we aim to describe the structure of the tethering complex and elucidate its pivotal role in renal I/R injury by summarizing its role in many important mechanisms,such as mitophagy,mitochondrial fission,mitochondrial fusion,apoptosis and necrosis,ER stress,mitochondrial substance transport,and lipid metabolism.
基金This work was supported by grants from the National Natural Science Foundation of China(Nos.61971441,61671479,and 81804056)the National Key R&D Program of China(No.2016YFC1305500).
文摘Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases.With the development of data preservation and processing,as well as the advancement of machine learning algorithms,machine learning is expected to make remarkable breakthroughs in nephrology.Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields,including analysis of renal pathological images,diagnosis and prognosis of chronic kidney diseases and acute kidney injury,as well as management of dialysis treatments.However,it is just scratching the surface of the field;at the same time,machine learning andits applications in renal diseases are facing a number of challenges.In this review,we discuss the application status,challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction,detection,and care quality in kidney diseases.
