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Abstract Background Alzheimer’s disease (AD) is one of the most burdening diseases of the century with no disease-modifying treatment at this time. Nonhuman primates (NHPs) share genetic, anatomical, and physiological similarities with humans, making them ideal model animals for investigating the pathogenesis of AD and potential therapies. However, the use of NHPs in AD research has been hindered by the paucity of AD monkey models due to their long generation time, ethical considerations, and technical challenges in genetically modifying monkeys. Methods Here, we developed an AD-like NHP model by overexpressing human tau in the bilateral hippocampi of adult rhesus macaque monkeys. We evaluated the pathological features of these monkeys with immunostaining, Nissl staining, cerebrospinal fluid (CSF) analysis, magnetic resonance imaging (MRI), positron emission tomography (PET), and behavioural tests. Results We demonstrated that after hippocampal overexpression of tau protein, these monkeys displayed multiple pathological features of AD, including 3-repeat (3R)/4-repeat (4R) tau accumulation, tau hyperphosphorylation, tau propagation, neuronal loss, hippocampal atrophy, neuroinflammation, Aβ clearance deficits, blood vessel damage, and cognitive decline. More interestingly, the accumulation of both 3R and 4R tau is specific to NHPs but not found in adult rodents. Conclusions This work establishes a tau-induced AD-like NHP model with many key pathological and behavioural features of AD. In addition, our model may potentially become one of the AD NHP models adopted by researchers worldwide since it can be generated within 2 ~ 3 months through a single injection of AAVs into the monkey brains. Hence, our model NHPs may facilitate mechanistic studies and therapeutic treatments for AD.

Abstract Background Diabetic kidney disease (DKD) is a severe complication of diabetes. Currently, no effective measures are available to reduce the risk of DKD progression. This study aimed to establish a weighted risk model to determine DKD progression and provide effective treatment strategies. Methods This was a hospital-based, cross-sectional study. A total of 1104 patients with DKD were included in this study. The random forest method was used to develop weighted risk models to assess DKD progression. Receiver operating characteristic curves were used to validate the models and calculate the optimal cutoff values for important risk factors. Results We developed potent weighted risk models to evaluate DKD progression. The top six risk factors for DKD progression to chronic kidney disease were hemoglobin, hemoglobin A1c (HbA1c), serum uric acid (SUA), plasma fibrinogen, serum albumin, and neutrophil percentage. The top six risk factors for determining DKD progression to dialysis were hemoglobin, HbA1c, neutrophil percentage, serum albumin, duration of diabetes, and plasma fibrinogen level. Furthermore, the optimal cutoff values of hemoglobin and HbA1c for determining DKD progression were 112 g/L and 7.2%, respectively. Conclusion We developed potent weighted risk models for DKD progression that can be employed to formulate precise therapeutic strategies. Monitoring and controlling combined risk factors and prioritizing interventions for key risk factors may help reduce the risk of DKD progression.

Abstract Background Diabetic foot ulcer (DFU) is a serious chronic complication of diabetes. This study aimed to establish weighted risk models for determining DFU occurrence and severity in diabetic patients. Methods This was a multi-center hospital-based cross-sectional study. A total of 1488 diabetic patients with or without an ulcer from three tertiary hospitals were included in the study. Random forest method was used to develop weighted risk models for assessing DFU risk and severity. Receiver operating characteristic curves were used to validate the models and calculate the optimal cut-off values of the important risk factors. Results We developed potent weighted risk models for evaluating DFU occurrence and severity. The top eight important risk factors for DFU onset were plasma fibrinogen, neutrophil percentage and hemoglobin levels in whole blood, stroke, estimated glomerular filtration rate, age, duration of diabetes, and serum albumin levels. The top 10 important risk factors for DFU severity were serum albumin, neutrophil percentage and hemoglobin levels in whole blood, plasma fibrinogen, hemoglobin A1c, estimated glomerular filtration rate, hypertension, serum uric acid, diabetic retinopathy, and sex. Furthermore, the area under curve values in the models using plasma fibrinogen as a single risk factor for determining DFU risk and severity were 0.86 (sensitivity 0.74, specificity 0.87) and 0.73 (sensitivity 0.76, specificity 0.58), respectively. The optimal cut-off values of plasma fibrinogen for determining DFU risk and severity were 3.88 g/L and 4.74 g/L, respectively. Conclusions We have established potent weighted risk models for DFU onset and severity, based on which precise prevention strategies can be formulated. Modification of important risk factors may help reduce the incidence and progression of DFUs in diabetic patients.