[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]. / åŸºäºŽå›½å®¶åŸºæœ¬å ¬å ±å«ç”ŸæœåŠ¡ä½“æ£€çš„ä¸­è€å¹´äºº2åž‹ç³–å°¿ç— é£Žé™©é¢„æµ‹æ¨¡åž‹æž„å»º.

Objective: To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service. Methods: Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive ß, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people. Results: A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined ß values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals. Conclusion: The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Carboxymethyl cellulose-based hydrogel with high-density crack microstructures inspired from the multi-tentacles of octopus for ultrasensitive flexible sensing microsystem.

Constructing high-density contact-separation sites on conductive materials highly determines the sensitivity of flexible resistance-type sensors relying on the crack microstructures. Herein, inspired from the multiple-tentacle structures on octopus, we demonstrated a sort of novel carbonized ZIF-8@loofah (CZL) as conductive material to develop ultrasensitivity flexible sensor, in which the carbonized ZIF-8 nanoparticles (~100 nm) served as tentacles. Originating from the formation of high-density contact-separation sites, the fabricated CZL-based strain sensor delivered ultrahigh sensitivity of GFmax = 15,901, short response time of 22 ms and excellent durability over 10,000 cycles. These features enable the sensor with efficient monitoring capacity for complex human activities, such as pulse rate and phonation. Moreover, when CZL was assembled into triboelectric nanogenerator (TENG), CZL-based TENG can effectively convert the irregular biomechanical energy into electric energy, providing sustainable power supply for the continuous operation of the sensing micro-system. Our findings established a novel platform to develop high-performance self-powered sensing systems of physiological parameter of human inspired from the nature.

Fecal microbiota transplantation regulates the microbiota-gut-spinal cord axis to promote recovery after spinal cord injury.

Spinal cord injury (SCI) is devastating for patients, and currently lacks effective treatments. Dysbiosis commonly occurs after SCI and has significant immunomodulatory effects, but its impact on recovery remains unclear. The current study investigated the effects and mechanisms of fecal microbiota transplantation (FMT) in SCI. FMT was administered in a rat model of SCI and spinal pathology, inflammatory cytokines, and gut microbiome composition were assessed. Flow cytometry identified a source of interleukin (IL)-17 in spinal cord tissues, and carboxyfluorescein succimidyl ester labeling tracked γδ T cell migration. In vitro coculture was used to analyze the regulatory mechanisms of γδ T cells. Seahorse analysis was used to profile dendritic cell (DC) metabolism. Here we show that FMT improved spinal pathology and dampened post-injury inflammation. It also corrected post-SCI dysbiosis, increasing levels of the beneficial bacterium Akkermansia. The therapeutic effects of FMT were mediated by IL-17 produced by γδ T cells. FMT regulated γδ T cells via DC-T regulatory cell interaction, and induced metabolic reprogramming in DCs. These findings suggest that FMT represents a promising therapeutic approach for SCI, with potential to target IL-17+ γδ T cells. Elucidating the interconnected pathways between microbiota, immunity, and the spinal cord may facilitate novel treatment strategies.