Objective:To systematically review the risk prediction models for cognitive impairment in stroke patients, aiming to provide references for clinical healthcare professionals in selecting or constructing high-quality risk assessment tools.Methods:A computerized search was conducted in PubMed, Embase, Web of Science, OVID, Cochrane Library, SinoMed, CNKI, Wanfang Database, and VIP to identify studies related to risk prediction models for cognitive impairment in stroke patients. The search was limited to articles published up to August 1, 2023. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies using PROBAST.Results:A total of 26 articles were included. The applicability of the studies was generally good, but all studies had some degree of bias risk, mainly arising from unreasonable study designs, inappropriate time intervals between predictor assessment and outcome determination, insufficient sample sizes, unreasonable handling of continuous variables, omission of missing data, lack of reporting of calibration, and overfitting of the models. Meta-analysis results showed that age ( OR=0.05, 95% CI: 0.033-0.057), education level ( OR=-0.13, 95% CI: -0.171 - -0.082), history of diabetes ( OR=2.32, 95% CI: 1.867-2.881), history of hypertension ( OR=0.67, 95% CI: 0.420-0.918), and NIHSS score ( OR=0.40, 95% CI: 0.331-0.469) were factors for cognitive impairment in stroke patients. Conclusions:While various risk prediction models for cognitive impairment in stroke patients exist, they suffer from methodological flaws and high bias risks, with some commonalities and controversies in predictors. Future research should adhere to the principles of transparent reporting of individual prognosis or diagnosis of multivariate prediction models, develop localized prediction models with low bias risk and good applicability, and conduct internal and external validations to demonstrate their applicability and feasibility in clinical practice.

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus, which is characterized in renal tubulointerstitial fibrosis (TIF). The current study was designed to investigate the protective effect of Jujuboside A (Ju A) on TIF in type 2 diabetes (T2DM) mice, and explore its underlying anti-fibrosis mechanism. A mouse T2DM model was established using high fat diet (HFD) feeding combined with intraperitoneal injection of streptozotocin (STZ). Then, diabetic mice were treated with Ju A (10, 20 and 40 mg·kg^-1·d^-1, i.g.) for 12 weeks. Results showed that administration of Ju A not only down-regulated fasting blood glucose (FBG) levels, but also improved hyperlipidemia and renal function in diabetic mice. Moreover, the reduced ECM accumulation was observed in the renal cortex of Ju A treated diabetic mice, while the TIF progression was also attenuated by Ju A through blocking the epithelial-to-mesenchymal transition (EMT) of renal tubular epithelial cells (RTECs). Further mechanism studies showed that Ju A treatment effectively down-regulated the protein expression and subsequent nuclear translocation of Yin Yang 1 (YY1) in the renal cortex of diabetic mice, and reduced the levels of transforming growth factor-β1 (TGF-β1) in the serum and renal cortex of Ju A treated mice. According to invitro studies, the up-regulated YY1/TGF-β1 signaling pathway was restored by Ju A in high glucose (HG) cultured HK-2 cells. Taken together, these findings demonstrated that Ju A can ameliorate the TIF of DN through down-regulating the YY1/TGF-β1 signaling pathway.
Animals ; Blood Glucose ; Diabetes Mellitus, Experimental/metabolism* ; Diabetes Mellitus, Type 2/drug therapy* ; Diabetic Nephropathies/metabolism* ; Fibrosis ; Mice ; Saponins ; Signal Transduction ; Streptozocin ; Transforming Growth Factor beta1/metabolism*