Objective To investigate the prevalence of common chronic diseases and comorbidities among people aged ≥15 years in Urumqi, and to provide a theoretical basis for the prevention and treatment of chronic diseases in the region. Methods Using population health surveillance data from 2016 to 2021, a retrospective analysis was performed on the prevalence of 15 chronic diseases among permanent residents aged 15 years and above in Urumqi. Results From 2016 to 2021, the detection rate of common chronic diseases among people aged 15 years or above in Urumqi was higher among the female, urban, ≥65 years, and overweight and obese populations. Among the 15 prevalent chronic diseases, dyslipidemia (fatty liver) had the highest detection rate, while hypertension, diabetes mellitus, cholecystitis, and coronary heart disease (myocardial infarction) ranked second to fifth, in common chronic disease detection rates. The prevalence of comorbidities among patients with chronic diseases was consistent with that of chronic diseases, with the overall prevalence of women being higher than that of men (χ²=127.31, P<0.001), and the prevalence in urban areas being higher than that in rural areas (χ²=16503.75, P <0. 001). The prevalence of comorbidities increased gradually with age (χ²=9179.36, P <0.001). The proportion of people with comorbidities who had abnormal BMI was significantly higher than that of people with normal BMI (χ²=21699.61, P <0.001). The proportion of chronic disease patients with single diseases increased gradually from 2016 to 2020 and decreased significantly by 2021, while the proportion of patients with comorbidities increased significantly by 2021. The number of comorbidity disease types in the study population showed an increasing trend year by year with age. Conclusion The detection rate of chronic diseases among people over 15 years old is high in Urumqi, and there is an increasing prevalence of comorbidities in patients. Chronic diseases, mainly dyslipidemia (fatty liver), hypertension, diabetes, cholecystitis and coronary heart disease (myocardial infarction) are highly prevalent. These findings indicate the need for the development of comprehensive prevention and treatment intervention measures.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Objective To investigate the status and influencing factors of pressuring feeding style a-mong caregivers in remote rural areas of Sichuan province.Methods Multistage sampling was conducted to select infants of 6-11 months old who had received complementary food and their caregivers in remote rural areas of Si-chuan province.A questionnaire was used to collect sociodemographic characteristics of infants and their caregivers,pressuring feeding behaviors,feeding environment,and caregivers'negative emotions.Quantile regression was em-ployed to analyze the factors influencing pressuring feeding among caregivers of infants.Results A total of 1358 pairs of infants and their caregivers were included,with the pressuring feeding behavior score of 11(8,14).Parity was the protective factor for caregivers'pressuring feeding(β25=-1.17,P＜0.001;β50=-1.40,P=0.002;β75=-2.18,P＜0.001).Whether infants played with toys while eating(β25=1.00,P＜0.001;β50=1.20,P=0.003;β75=1.42,P＜0.001)and whether infants watched TV/mobile phones(β25=0.50,P=0.048;β50=1.07,P=0.004)were the risk factors.At the 75th percentile,caregivers'negative emotions were the risk factor for pressuring feeding(β75=0.94,P=0.015).Caregivers'education background(β25=0.83,P=0.034;β50=0.87,P=0.021)and family income(β75=1.09,P=0.012)were also significantly associated with pressuring feeding scores at different quartile points.Conclusions Pressuring feeding behaviors of caregivers in remote rural areas of Sichuan province need to be improved.Based on the characteristics of infants and their families,guidance should be carried out to improve the feeding environment and the mental health of caregivers,thereby promoting reasonable feeding behaviors among caregivers of infants in rural areas.

Purpose/Significance By investigating and analyzing the characteristics of scientific data citation of scientific data sharing institutions in the field of medical and population health,the paper provides references and guidance for open sharing and standardized ci-tation of scientific data.Method/Process Taking the National Population Health Data Center as an example,the literature analysis meth-od is used to analyze the scientific data citation behavior and its traceability in this field.Result/Conclusion It is found that there are non-standard citation behaviors such as inconsistent labeling positions and missing citation elements in the scientific data in the field of medical and population health,and a large number of open and shared scientific data cannot be effectively traced.It is urgent to improve the standards of scientific data citation,strengthen the management of scientific data platform,enhance the awareness of scientific re-searchers,and promote scientific data sharing and citation.

The fundamental pathogenic mechanisms of inflammatory bowel disease(IBD),a chronic inflammatory illness affecting the gastrointestinal tract,are still not fully understood.Dipeptidyl peptidase Ⅳ(DPP-4)is a glycoprotein found on the cell surface that has signaling and enzymatic properties.In addition,there is growing evidence from animal models and clinical trials that DPP-4 inhibitors have potential impacts beyond only lowering blood sugar levels,such as immunomodulatory and anti-inflammatory capabilities.This article reviews the biological properties of DPP-4 and its research progress in the diagnosis and treatment of IBD.It also discusses the mechanism of DPP-4 in IBD,aiming to provide a more in-depth understanding of the role of DPP-4 in IBD.

Objective To explore the application of seasonal autoregressive integrated moving average (ARIMA) model in the prediction of brucellosis in Urumqi, and to use this model to predict the incidence trend of brucellosis in Urumqi. Methods The monthly incidence data of brucellosis in Urumqi from January 2010 to December 2021 were selected to construct the ARIMA prediction model. The prediction effect of the model was evaluated by mean standard deviation (RMSE) and mean absolute error (MAE). The monthly incidence of brucellosis in Urumqi in 2022 was predicted by the constructed model. Results The incidence of brucellosis in Urumqi had obvious seasonal distribution, and the cases were concentrated from May to July. ARIMA(1,1,1)(1,0,1)12 was the optimal prediction model, with RMSE=0.883 and MAE=5.24. The monthly incidence of brucellosis in Urumqi in 2022 was predicted to be 7, 4, 4, 6, 9, 9, 10, 7, 7, 5, 5, and 5 cases, respectively. Conclusion ARIMA model can well fit and predict the monthly incidence of brucellosis in Urumqi and provide a basis for the monitoring and prevention of brucellosis.

Objective To investigate the epidemiological characteristics of foodborne diseases in Liuzhou City, and to provide reference for formulating the prevention and control measures of foodborne diseases. Methods The surveillance data of foodborne diseases in 25 sentinel hospitals in Liuzhou City from 2018 to 2020 were collected for statistical analysis. Results From 2018 to 2020, a total of 9 317 cases of foodborne diseases were reported in Liuzhou City, and 2 158 samples were collected for pathogen detection. A total of 311 cases were detected positive , with a detection rate of 14.41%. Norovirus had the highest detection rate (8.63%), followed by Salmonella (4.08%) and Escherichia coli (3.10%). July to October was a period of high incidence of foodborne disease（ 41.17%）. The proportion of patients aged 60 and over was the highest (18.49%), followed by the age group of 30-39 (18.03%). Suspicious foods were mainly meat and meat products (22.35%) and aquatic animals and their products (13.89%). The suspicious eating places were mainly families (40.43%) and restaurants (13.63%). Conclusion The high incidence of foodborne diseases in Liuzhou occurs in summer and autumn. The main pathogens are Salmonella and norovirus. Infected patients are concentrated in the age group of 60 years and above and the age group of 30 to 39 years old. The family is the main place of foodborne disease, followed by the restaurants and hotels. Suspicious foods include mainly meat and meat products and aquatic animals and their products. It is necessary to strengthen monitoring ability and food safety education to reduce the occurrence of foodborne diseases.

Objective:To investigate the predictive ability of Glypican-3 (GPC3) positive hepatocellular carcinoma based on the hepatobiliary specific contrast agent gadoxetate disodium enhancement of the liver imaging reporting and data system version 2018 (LI-RADS v2018) imaging features, and to assess the relevant clinical imaging features for postoperative recurrence in GPC3 positive HCC patients.Methods:This study was a cohort study. A total of 122 hepatocellular carcinoma patients who underwent gadoxetate disodium enhanced MRI examination with hepatic tumor resection in Henan Provincial People′s Hospital from January 2017 to December 2021 were retrospectively collected, including 96 GPC3 positive and 26 GPC3 negative patients. The imaging features defined by LI-RADS v2018 of HCC lesions were analyzed. Patients were followed up for 40 months to determine recurrence free survival (RFS). The logistic regression was used to analyze the risk factors of GPC3 positivity. An imaging model, and a clinical-imaging model which combined the patient′s alpha-fetoprotein levels were constructed. The efficacy of the model for predicting GPC3 positivity was assessed using receiver operating characteristic curves. Kaplan-Meier method was used to draw the survival curve, and the log-rank test was used to compare the RFS between GPC3 positive and negative patients. Risk factors affecting the recurrence of GPC3 positive HCC were assessed by Cox regression.Results:The results of logistic multivariate regression analysis confirmed that rim enhancement ( OR=5.685, 95% CI 1.229-26.287, P=0.026) and irregular tumor margin at hepatobiliary phase ( OR=4.431, 95% CI 1.684-11.663, P=0.003) were independent risk factors for GPC3 positive HCC. The area under the curve for predicting GPC3 positivity was 0.745 (95% CI 0.636-0.854) for the imaging model and 0.776 (95% CI 0.677-0.876) for the clinical-imaging model. The mean RFS in the GPC3 positive group was 22 months, and it was 32 months in the negative group. There was a statistically significant difference in RFS between the two groups ( χ2=5.15, P=0.023). The multivariate Cox regression analysis showed that the arterial rim enhancement ( HR=5.460, 95% CI 1.966-15.162, P=0.001), microvascular invasion ( HR=2.402, 95% CI 1.210-4.769, P=0.012), portal vein tumor thrombus ( HR=3.226, 95% CI 1.114-9.344, P=0.031) were independent risk factors for recurrence after hepatic tumor resection for GPC3-positive HCC. Conclusions:A model based on the LI-RADS v2018 imaging features of hepatobiliary specific contrast agent gadoxetate disodium enhancement can effectively predict GPC3 positive HCC. The arterial rim enhancement, microvascular invasion and portal vein tumor thrombus are independent risk factors for postoperative recurrence of GPC3 positive HCC.

Objective To sort out the scientific research achievements in the direction of early warning of infectious diseases in China from 2001 to 2022, and analyze the research hotspots and trends in the direction of early warning of infectious diseases in China in recent 20 years, so as to provide reference for relevant policies and exploration directions. Methods Relevant literature retrieved from CNKI Chinese database was used as the data source, and Excel 2019 and Citespace 6.1.R2 software were used for visual analysis of research hotspots and frontier literature. Results A total of 1276 papers meeting requirements were obtained, and most of the research groups were relatively small and had little cooperation with others. The types of research institutions were relatively single, and most of them were domestic universities. ^“Infectious diseases^”, ^“early warning^” and ^“prediction^” were the most frequently used keywords. Research on big data and COVID-19 epidemic prevention and control is the current research frontier. Conclusion There is little cooperation among authors and between institutions in the field of early warning of infectious diseases in China. Using big data to early warning of infectious diseases and improving the ability of early warning of COVID-19 are the main research directions and trends at present.