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

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.

Objective To analyze the mutation status of Kirsten rat sarcoma viral oncogene homolog(KRAS),phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3CA),v-raf murine sarcoma viral oncogene homolog B1(BRAF)genes,and the expression of mismatch repair(MMR)and human epidermal growth factor receptor 2(HER-2)proteins in tumor tissues of patients with colorectal cancer(CRC)harboring neuroblastoma rat sarcoma viral oncogene homolog(NRAS)gene mutations,and explore their relationships with the clinicopathological characteristics of CRC patients.Methods The clinicopathological data of 546 patients with NRAS mutation CRC were retrospectively analyzed.The mutation status of NRAS,KRAS,PIK3CA,and BRAF genes was detected by AmoyDx amplification refractory mutation system(ARMS)-polymerase chain reaction(PCR)kit(fluorescent PCR method),the expression levels of MMR and HER-2 proteins were detected by immunohistochemical staining EnVision method,and the relationship between them and the clinicopathological characteristics of patients were analyzed.Results The mutation rate of single-point mutations in the NRAS gene was 98.35%(537/546),double-point mutations in the NRAS gene were 1.65%(9/546),and double mutations in the NRAS and KRAS genes were 1.47%(8/546).No patients were found to harbor mutations in the PIK3CA or BRAF genes.The types of NRAS mutations included Q61R(or Q61K,Q61L,Q61H)mutations(266/546,48.72%),G12D(or G12S)mutations(154/546,28.21%),G13R(or G12C,G12V,G12A,G13V)mutations(134/546,24.54%),and A146T mutation(1/546,0.18%).G13R(or G12C,G12V,G12A,G13V)mutations in the NRAS gene were more likely to occur in the rectum cancer patients(P=0.035);although the tumors had a larger diameter(P=0.029),the patients had a longer progression-free survival after surgery(P=0.028).Among patients with NRAS gene mutations,HER-2 positive expression was associated with perineural invasion(P=0.003),and the patients with deficient MMR were younger on average(P=0.041)and were associated with double-point mutations in the NRAS gene(P=0.018).Conclusion CRC harboring NRAS mutations may have unique clinicopathological characteristics and molecular phenotypes,providing possibilities for individualized treatment and prognosis evaluation of CRC.

Objective:To construct a prognostic risk model based on lactate metabolism-related genes screened using bioinformatics methods in renal cell carcinoma patients，and investigate the clinical prognosis and immune characteristics of renal cell carcinoma.Methods:Gene expression data and clinical information of patients with renal cell carcinoma were downloaded from the Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma（TCGA-KIRC）dataset. Lactate metabolism-related gene sets were obtained from the Gene Set Enrichment Analysis（GSEA）database. The R package DEseq2 was employed to identify differentially expressed genes associated with lactate metabolism within the TCGA-KIRC dataset. GO and KEGG enrichment analyses were performed using the clusterProfiler package. Prognosis-related genes were screened via univariate Cox regression analysis and the intersection with lactate metabolism-related differentially expressed genes was obtained. A risk model was constructed using LASSO regression followed by multivariate Cox regression analysis to calculate risk scores. This risk model was subsequently validated using the GSE29609 dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. The expression profiles of key immune molecule genes and immune checkpoint genes were compared between the two groups. Survival analysis curves for immune checkpoint genes were generated using the survival and survminer R packages. Differences in tumor mutation burden（TMB）between the high-risk and low-risk groups were assessed，and corresponding TMB survival analysis curves were plotted. Finally，the tumor immune dysfunction and exclusion（TIDE）algorithm was used to evaluate disparities in immunotherapy response potential between the two risk groups.Results:An optimal prognostic risk model incorporating seven lactate metabolism- and prognosis-related genes（ LDHD，PER2，ACADM，FLI1，LIPA，TCIRG1，SLC25A4）was constructed and successfully validated in the GSE29609 dataset. Univariate Cox regression analysis revealed that a high-risk score was significantly associated with poor prognosis（ HR=2.915，95% CI：2.451-3.470， P<0.001）. Multivariate Cox regression analysis confirmed that this risk model could serve as an independent prognostic factor for patients with renal cell carcinoma（ HR=2.231，95% CI：1.829-2.722， P<0.001）. Patients in the high-risk group exhibited significantly worse outcomes compared to the low-risk group，regardless of whether they had early-stage or advanced-stage renal cell carcinoma（both P<0.001）. Analyses related to the immune microenvironment indicated an upregulated immunosuppressive phenotype in the high-risk group. Furthermore，the TMB was significantly higher in the high-risk group than in the low-risk group（ P=0.032），and patients within the high-risk group exhibiting higher TMB levels demonstrated even poorer survival（ P<0.001）. Finally，the TIDE score was significantly elevated in the high-risk group in comparison to the low-risk group（ P<0.001）. Conclusions:The risk model based on lactate metabolism-related genes constructed in this study can guide the prognosis of renal cell carcinoma. Patients in the high-risk group are more prone to immune escape and formation of an inhibitory immune microenvironment，leading to worse prognoses. This risk model may serve as a biomarker for predicting immunotherapy response.

Objective To establish a rat model of postoperative delirium(POD)induced by cardiopulmonary bypass with human gut microbiota using fecal microbiota transplantation(FMT)technology,and evaluate the model based on bioinformatics,cytokine analysis,and behavioral testing methods.Methods SPF-grade adult male SD rats weighing 400 to 450 g were selected.After under-going a week of Morris water maze training,rats with consistent performance were used to construct pseudo-germ-free rat models.Subsequently,20 successfully modeled rats were randomly divided into two groups:(CON group receiving fecal microbiota filtrate from healthy individuals)and(POD group receiving fecal microbiota filtrate from POD patients).Behavioral tests were conducted two weeks af-ter modeling,and rat feces were collected for metagenomic sequencing.Rats were euthanized by cer-vical dislocation,and blood and brain tissue samples were collected for cytokine and histopathological examinations.Results Compared with the CON group,the POD group exhibited significantly increased relative abundances of Akkermansiaceae,Prevotellaceae,and Akkermansia muciniphila,while the relative abundances of Lactobacillaceae and Mediterraneibacter massiliensis decreased significantly(P＜0.05).Serum levels of interleukin(IL)-1β,IL-6,and tumor necrosis factor(TNF)-α were signif-icantly higher in the POD group than those in the CON group(P＜0.05).Hematoxylin-eosin(HE)staining in the POD group revealed neurons with pyknotic and hyperchromatic nuclei.After modeling,the average latency in the Morris water maze was significantly longer in the POD group than that in the CON group(P＜0.05).Conclusion This study utilizes fecal microbiota trans-plantation technology to establish a rat model of POD induced by cardiopulmonary bypass with hu-man gut microbiota.The changes in gut microbiota structure abundance,levels of POD-related in-flammatory factors,and Morris water maze test results in this model are similar to the clinical mani-festations observed in patients with POD induced by cardiopulmonary bypass.