Area of research
Artificial Intelligence · Nephrology
Research interest
Research topics from publications: Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators; Prediction of breast cancer metastasis by deep learning pathology; Exploratory metabolomic analysis based on UHPLC-Q-TOF-MS/MS to study hypoxia-reoxygenation energy metabolic alterations in HK-2 cells; A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus; MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework; Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning; Bone marrow inhibition induced by azathioprine in a patient without mutation in the thiopurine S-methyltransferase pathogenic site: A case report; Blockage of TIM-3 relieves lupus nephritis by expanding Treg cells and promoting their suppressive capacity in MRL/lpr mice; Prediction of favorable outcomes of acute basilar artery occlusion using machine learning. Representative work: Abstract With the rapid development of social economy, the incidence of breast cancer is increasing year by year. Whether there is lymph node metastasis in frozen tissue sections during breast cancer surgery is of tremendous priority for breast cancer surgical decision‐making. Therefore, it is very significant to diagnose the pathological sections of breast cancer quickly and accurately. In this study, a model which can quickly fine segmentation of lesion regions in high‐resolution breast cancer pathology sections is proposed. Firstly, pathology sections are processed by pre‐processing module; Secondly, the main lesion region in pathology sections can be quickly recognized by recognition mod PURPOSE: Renal ischemia-reperfusion injury(IRI)is a major cause of acute kidney injury(AKI), the injury and repair of renal tubular epithelial cells play an important role in the pathological process of IR-AKI. Metabolomics was used to detect cell metabolism alterations and metabolic reprogramming in the initial injury, peak injury, and recovery stage of human renal proximal tubular cells (HK-2 cells) to provide insights into clinical prevention and treatment of IRI-induced AKI. METHODS: ischemia-reperfusion (H/R) injury and the recovery model of HK-2 cells were established at different times of hypoxi
Prediction of favorable outcomes of acute basilar artery occlusion using machine learning
MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework
Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators
Exploratory metabolomic analysis based on UHPLC-Q-TOF-MS/MS to study hypoxia-reoxygenation energy metabolic alterations in HK-2 cells
A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning
Prediction of breast cancer metastasis by deep learning pathology
Blockage of TIM-3 relieves lupus nephritis by expanding Treg cells and promoting their suppressive capacity in MRL/lpr mice
Bone marrow inhibition induced by azathioprine in a patient without mutation in the thiopurine S-methyltransferase pathogenic site: A case report