Aim Todeterminetherelationshipbe-tween serum TNF-α and renal abnormal lipid metabo-lismindiabeticmice.Methods CD1micewerein-jected intraperitoneally with STZ (150 mg·kg-1 ) and the type 1 diabetic mice were determined with high fasting blood glucose ( >16. 7 mmol · L-1 ) 72 hours after injection. After fed for one month, normal control mice and diabetic mice were sacrificed. The serum TNF-α level was detected by the method of ELISA. Immunohistochemistry and Western blot were used to explore the expression of SREBP-1 and ADRP. Re-sults TheresultsofELISAshowedthatserumTNF-αwas increased by 7 . 73 times in diabetic mice compared with normal mice. SREBP-1 and ADRP expressions were located in renal tubular cells. Again, it was con-firmed by Western blot that SREBP-1 precursor seg-ment, SREBP-1 mature segment and ADRP were re-spectively enhanced by 2. 31 times, 1. 74 times and 1. 72 times in diabetic mice in comparison with normal control mice . In addition , the data analysis revealed a
positive correlation ( correlation coefficient 0. 914 ) be-tween serum TNF-αand renal ADRP expression. Con-clusion TheincreasedserumTNF-αmaybeoneof factors involved in renal lipid accumulation of diabe-tes.

Purpose To investigate the expression of XBP-1s and ADRP in kidney of diabetic rats. Methods Diabetic rat models were successfully established by intraperitoneal injection of STZ. After two months rats were sacrificed and XBP-1s and ADRP were de-tected by immunohistochemistry and Western blot. Results XBP-1s and ADRP were located in renal tubular cells and increased by a-bout 2. 017 times and 1. 544 times in comparison with normal control rats (P<0. 05). Moreover, it was shown that high expression of XBP-1s was commonly accompanied with increased ADRP by Pearson correlation analysis and the correlation coefficient was 0. 723 (P<0. 05). Conclusion The increased XBP-1s may cause the up-regulation of ADRP in the kidney of diabetic rats.

Objective:To explore the value of coronary artery plaque progression parameters based on coronary CT angiography (CCTA) in predicting the occurrence of major adverse cardiovascular events (MACE) in patients with non-obstructive coronary artery disease.Methods:The study included clinical, imaging, and prognosis (MACE) parameters of non-obstructive coronary artery disease patients who underwent CCTA at the First Affiliated Hospital of Nanjing Medical University from September 2010 to December 2022. Patients were grouped based on the occurrence of MACE, and differences in clinical data, plaque baseline, and progression parameters between the two groups were compared. Univariate and multivariate Cox regression analyses were employed to identify factors that could effectively predict the occurrence of MACE in patients. Models were constructed using plaque baseline parameters, plaque progression parameters, and a combination of both. The concordance index-time curve, net reclassification improvement and integrated discrimination improvement were used to evaluate the risk stratification ability of the models.Results:A total of 258 patients were included, of whom 62 cases experienced MACE during the follow-up period. In comparison to the MACE(-) group, patients in the MACE(+) group exhibited longer lesion length, greater degree of stenosis, larger plaque total volume, calcified plaque volume, non-calcified plaque volume, fibrous plaque volume, total plaque burden, lipid-rich plaque burden, higher peri-coronary adipose tissue attenuation index (FAI), and annual change of diameter stenosis(ΔDS/y). There were also more cases of coronary artery disease reporting and data system upgrades and non-obstructive progression to obstructive status ( P<0.05). Multivariate Cox analysis revealed that FAI, ΔDS/y, and non-obstructive progression to obstructive status were independent predictors of MACE occurrence. Concordance index-time curve results indicated that the combined model had a better predictive efficacy for MACE in patients with non-obstructive coronary artery disease compared to models based on plaque baseline parameters and plaque progression parameters. Conclusion:The plaque progression parameters and FAI based on CCTA have the potential to predict the high-risk population for MACE in patients with non-obstructive coronary artery disease, demonstrating good risk stratification value.