Objective: To investigate the implementation and evaluate the effectiveness of health management services for patients with type 2 diabetes mellitus (T2DM) in Urumqi City from 2017 to 2021, so as to provide the reference for enhancing T2DM patients management. Methods: Data on the rates of record establishment, health management and standardized management for T2DM patients, and blood glucose control rate in managed patients in Urumqi City from 2017 to 2021 were collected through the quarterly reports on basic public health service management from the Weining Health Platform System. The trends in the four management indicators, and the differences between urban and rural areas were analyzed. Results: The rates of record establishment, health management and blood glucose control rate in managed patients in Urumqi City were 46.94%, 38.37% and 59.92%, respectively, showing upward trends from 2017 to 2021 (all P<0.05). The rate of standardized management was 75.89%, showing a downward trend (P<0.05). The rate of record establishment was higher in urban areas than in rural areas (47.76% vs. 40.56%, P<0.05). The rates of standardized management and blood glucose control in managed patients were lower in urban areas than in rural areas (75.18% vs. 81.46%, 58.93% vs. 67.64%, both P<0.05). The rate of health management was 38.39% in urban areas and 38.24% in rural areas, with no statistically significant difference (P>0.05). The trends in the rates of record establishment, health management and standardized management in both urban and rural areas were consistent with those in the overall population. Conclusions From 2017 to 2021, the rates of record establishment, health management and blood glucose control in managed patients in Urumqi City showed upward trends, while the rate of standardized management exhibited a downward trend. There were urban-rural differences in the rates of record establishment, standardized management and blood glucose control in managed patients.

The incidence of type 2 diabetes mellitus (T2DM) has been continuously rising, severely impacting health and increasing the medical burden. With the development of medical big data and artificial intelligence, research into constructing T2DM and its complications prediction models using machine learning methods based on multidimensional data such as genetic information, health records and laboratory testing data have increased, providing new ideas and means for the prevention and control of T2DM. This article reviewed the research progress in prediction models related to the risk of T2DM to understand the classification, modeling methods and applications by retrieving literature on T2DM and its complications prediction models from domestic and international databases including CNKI, Web of Science, and PubMed from 2003 to 2024, so as to provide the reference for early screening and intervention of T2DM.

In different stages of schizophrenia (SZ), alterations in gray matter volume (GMV) of patients are normally regulated by various pathological mechanisms. Instead of analyzing stage-specific changes, this study employed a multivariate structural covariance model and sliding-window approach to investigate the illness duration-related developmental trajectory of GMV in SZ. The trajectory is defined as a sequence of brain regions activated by illness duration, represented as a sparsely directed matrix. By applying this approach to structural magnetic resonance imaging data from 145 patients with SZ, we observed a continuous developmental trajectory of GMV from cortical to subcortical regions, with an average change occurring every 0.208 years, covering a time window of 20.176 years. The starting points were widely distributed across all networks, except for the ventral attention network. These findings provide insights into the neuropathological mechanism of SZ with a neuroprogressive model and facilitate the development of process for aided diagnosis and intervention with the starting points.
Humans ; Schizophrenia/pathology* ; Gray Matter/pathology* ; Magnetic Resonance Imaging ; Disease Progression ; Male ; Female ; Brain/pathology* ; Cerebral Cortex/pathology* ; Adult

During the complete denture restoration process, accurate impression making is a crucial step for achieving good denture retention. With the increasing popularity of intraoral scanning technology in fixed restoration, the use of intraoral scanning technology in complete denture restoration for edentulous jaw has also been developed. This article systematically reviews the research progress and application of intraoral scanning of edentulous soft tissue, focusing on difficulties in intraoral scanning of edentulous jaws, scanning accuracy, clinical application effects, as well as precautions involved. The aim is to provide references for clinical application.

Objective:To explore the causal association of glucose-lipid metabolism and obesity indicators with myocardial infarction by a two-sample Mendelian randomization analysis.Methods:Single nucleotide polymorphisms (SNPs) related to phenotypes were obtained from genome-wide association study databases. The body mass index (BMI) and glycated hemoglobin dataset includes 99 998 samples and 8 126 035 SNPs; the waist-to-hip ratio dataset includes 224 459 samples and 2 562 516 SNPs; the waist circumference and hip circumference dataset includes 462 166 samples and 9 851 867 SNPs; the fasting glucose dataset includes approximately 12 million SNPs; the low-density lipoprotein cholesterol (LDL-C) dataset includes 201 678 samples and 12 321 875 SNPs; the high-density lipoprotein cholesterol (HDL-C), and triglycerides dataset includes 156 109 samples and 15 784 414 SNPs; and the body fat percentage, whole-body fat mass, trunk fat percentage, and trunk fat mass dataset includes 454 588 samples and 9 851 867 SNPs. This study primarily used inverse-variance weighted method to analyze the associations between various exposure factors and outcomes. Heterogeneity among SNPs was assessed using Cochran′s Q test, and horizontal pleiotropy of SNPs was examined using the MR-Egger method. Additionally, a multivariable MR approach was used to adjust for BMI, further validating associations between exposure factors and the risk of myocardial infarction. Results:Higher BMI ( OR=1.070, 95% CI: 1.041-1.100), waist-to-hip ratio ( OR=1.366, 95% CI: 1.113-1.677), LDL-C ( OR=1.638, 95% CI: 1.488-1.803), triglycerides ( OR=1.445, 95% CI: 1.300-1.606), waist circumference ( OR=1.841, 95% CI: 1.650-2.055), hip circumference ( OR=1.247, 95% CI: 1.132-1.372), body fat percentage ( OR=1.795, 95% CI: 1.568-2.055), whole-body fat mass ( OR=1.519, 95% CI: 1.381-1.670), trunk fat percentage ( OR=1.538, 95% CI: 1.374-1.723), and trunk fat mass ( OR=1.421, 95% CI: 1.294-1.561), as well as lower HDL-C ( OR=0.799, 95% CI: 0.729-0.875), have causal effects on myocardial infarction (all P<0.05). After adjusting for BMI, hip circumference, trunk fat percentage, and trunk fat mass were no longer associated with myocardial infarction. However, waist-to-hip ratio ( OR=1.457, 95% CI: 1.132-1.877), fasting glucose ( OR=1.191, 95% CI: 1.024-1.383), glycated hemoglobin ( OR=1.129, 95% CI: 1.034-1.233), LDL-C ( OR=1.592, 95% CI: 1.314-1.929), triglycerides ( OR=1.410, 95% CI: 1.279-1.553), waist circumference ( OR=1.922, 95% CI: 1.448-2.551), body fat percentage ( OR=1.421, 95% CI: 1.072-1.884), and whole-body fat mass ( OR=1.295, 95% CI: 1.031-1.626) remained positively associated with myocardial infarction, while HDL-C ( OR=0.809, 95% CI: 0.729-0.897) remained negatively associated. Conclusions:Abdominal obesity and dysregulation of glucose-lipid metabolism are risk factors for myocardial infarction. Screening for glucose-lipid metabolism (fasting glucose, HDL-C, LDL-C, triglycerides) and obesity-related indicators (waist circumference, waist-to-hip ratio, body fat percentage, and whole-body fat mass) is of great importance for the primary prevention of myocardial infarction.

Objective Starting from the actual numbers of health personnel of tertiary public hospitals of Traditional Chinese Medicine(TCM),to quantitatively analyze the influencing factors on the allocation of human resources and obtain a prediction model.Methods The balanced panel data from 517 Tertiary Public TCM Hospitals in the period of 2011-2020 were collected,and the two-way fixed effects model was used to empirically analyze the impact of scale,demand and other factors on the actual number of health personnel in these hospitals.Result The number of beds is a key factor affecting the human resource allocation of Public TCM Hospitals,and various factors such as de-mand,policy,price,efficiency,and administrative management also have significant impacts on the allocation.The demand for outpatient services,government financial support,and efficiency of resource utilization are all promoting factors,while the increase in human resource prices,income generation efficiency,and administrative manage-ment levels have negative effects.A prediction model is proposed.Conclusion The planning principle of matching bed numbers with human resources allocation is in line with the actual environment.When predicting the total personnel allocation or authorized strength,various factors should also be fully considered,which can provide reference for the formulation of human resource policies in Public TCM Hospitals.