Objective To evaluate the efficiency of intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms for predicting benign and malignant Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions, and provide insights into early diagnosis of breast cancer. Methods A retrospective analysis was conducted based on the medical records of 450 female patients who underwent breast ultrasound examination in the Affiliated Cancer Hospital of Xinjiang Medical University from June 2020 to April 2022. The patients were divided into the benign (n = 199) and malignant (n = 195) groups according to pathological examination, and randomized into the training (n = 275) and validation (n = 119) sets at a ratio of 7∶3. Radiomics features were extracted and screened. Intra-tumor, peri-tumor, and intra-tumor + peri-tumor ultrasound radiomics models were constructed based on three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP). Receiver operating characteristics (ROC) curves, calibration curves, and decision curves were plotted to evaluate the efficacy of the radiomics models for prediction of benign and malignant breast lesions. Results A total of 17 intra-tumor, 16 peri-tumor, and 17 intra-tumor + peri-tumor radiomics features were selected for model construction. Based on LR, MLP, and SVM algorithms, the intra-tumor + peri-tumor radiomics models showed higher predictive efficacy than intra-tumor and peri-tumor radiomics models. The predictive efficacy of intra-tumor, peri-tumor, and intra-tumor + peri-tumor radiomics models were higher based on the SVM algorithm than based on LR and MLP algorithms. For the intra-tumor radiomics model based on the SVM algorithm, the area under the ROC curve (AUC), accuracy, sensitivity, and a specificity were 0.909, 0.851, 0.860, and 0.842, respectively, in the training set and 0.866, 0.832, 0.847, and 0.817, respectively, in the validation set. For the peri-tumor radiomics model based on the SVM algorithm, these values were 0.899, 0.855, 0.882, and 0.827, respectively, in the training set and 0.844, 0.815, 0.847, and 0.783, respectively, in the validation set. For the intra-tumor + peri-tumor radiomics model based on the SVM algorithm, these values were 0.943, 0.876, 0.860, and 0.892, respectively, in the training set and 0.881, 0.849, 0.915, and 0.783, respectively, in the validation set. Conclusion The intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms are highly valuable for prediction of benign and malignant BI-RADS 4 breast lesions. The intra-tumor + peri-tumor ultrasound radiomics model based on the SVM algorithm has the optimal efficacy for prediction of benign and malignant BI-RADS 4 breast lesions.

Objective:To investigate the relationship between different obesity metabolic phenotypes and the incidence of new carotid artery plaque.Methods:The present study is a retrospective cohort study, collecting individuals from the Health Management Center of the Second Affiliated Hospital of Dalian Medical University who had two or more cervical vascular color ultrasound examinations and met the inclusion criteria from 2014 to 2022, and collected their baseline clinical data. According to whether the subjects were obese and had metabolic syndrome, they were divided into metabolically healthy non-obese group, metabolically unhealthy non-obese group, metabolically healthy obese group, and metabolically unhealthy obese group. The first physical examination time of the subjects was taken as the starting point of follow-up, and cervical vascular color ultrasound was performed during the follow-up physical examination, with the outcome event being carotid artery plaque. Kaplan-Meier survival curve analysis was used to analyze the cumulative incidence of carotid artery plaques in the four groups and log-rank test was performed, and a multifactorial Cox proportional hazards model was used to analyze the relationship between different obesity metabolic phenotypes and the risk of carotid artery plaque incidence.Results:A total of 4 890 subjects were enrolled, aged (45.4±9.6) years, and 2 754 (56.3%) males. The follow-up time was 1.14(0.93, 2.20) years. Compared with the other 3 obesity metabolic phenotypes, the incidence of carotid plaques in the metabolically unhealthy obesity group was the highest (15.4% (286/1 861)). Kaplan-Meier survival curve analysis showed that the cumulative incidence of carotid plaques in metabolically unhealthy obese subjects was about 2.962 times that of metabolically healthy non-obese subjects (log-rank P<0.001). Multivariate Cox regression results showed that the risk of carotid plaque in metabolically unhealthy obese subjects was 1.650 times that of metabolically healthy non-obese subjects (95% CI: 1.203-2.264, P=0.002). Conclusion:Metabolically unhealthy obesity phenotype is an independent risk factor for carotid plaque.

Objective:To study the correlation between different body weight metabolic phenotypes and their changes and new-onset hyperuricemia in physical examination population.Methods:This study was a retrospective cohort study. A total of 31 956 people who underwent routine physical examination and met the inclusion and exclusion criteria at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 1, 2014 to August 31, 2022 were selected as the study subjects to establish a dynamic physical examination cohort. The end point of follow-up was new-onset hyperuricemia or the end of follow-up period. Cox regression stepwise fitting model was used to analyze the risk of different body weight metabolic phenotypes and hyperuricemia, and stratified analysis was performed for gender. According to body weight metabolic phenotype, the subjects were divided into normal metabolism and normal weight(NMNW) group, normal metabolism and obesity (NMO) group, abnormal metabolism and normal weight (AMNW) group and abnormal metabolism and obesity (AMO) group. The risk of hyperuricemia was calculated according to the changes of body weight metabolic phenotype during the follow-up period. In the sensitivity analysis, the robustness of the results was verified by changing the diagnostic criteria for hyperuricemia, removing patients with hyperuricemia at the first year of follow-up, and removing subjects aged ≥65 years.Results:Compared with the NMNW group, the risk of hyperuricemia in the NMO group, AMNW group and AMO group increased by 78.9%, 61.3%, 115.4%, respectively ( χ2=272.88, 128.15, 496.12, all P<0.001). Patients who were initially classified as NMNW at baseline, if transitioned to NMO or AMO by the follow-up endpoint, their risk of hyperuricemia increased by 122.5% ( χ2=8.01, P<0.05) and 137.4% ( χ2=15.99, P<0.001), respectively. When the baseline AMNW group changed to AMO, the risk of hyperuricemia was increased by 119.2% ( χ2=6.63, P<0.05). For patients with AMO as baseline, if they turned into NMNW and AMNW at the end of follow-up, their risk of hyperuricemia would decrease by 58.3% ( χ2=43.67, P<0.001) and 27.2% ( χ2=16.07, P<0.001). Patients with a baseline of NMO who transitioned to NMNW and AMNW at the follow-up endpoint had their risk of developing hyperuricemia decreased by 36.7% ( χ2=25.35, P<0.001) and 30.9% ( χ2=9.70, P<0.05), respectively. Conclusions:The transition from metabolic health and non-overweight obesity to metabolic abnormalities and overweight obesity is associated with an increased risk of hyperuricemia, and improvements in metabolic health or weight are associated with a decreased risk of hyperuricemia.

Objective:To explore the relationship between weight change and the development of hyperuricemia (HUA) in adults receiving health checkups.Methods:A retrospective cohort study. A total of 37 722 subjects who underwent two or more health checkups at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2022 were included, and the general information and laboratory findings at the time of the initial health checkups and follow-up were collected. Weight change was defined as the ratio of difference between the weight at the last follow-up and the baseline weight to baseline weight. The subjects were grouped with weight change: significant weight loss group (weight change ≤-5.0%), mild weight loss group (-5.0%

Objective:To investigate the association between different dietary patterns and subtypes of metabolic associated fatty liver disease(MAFLD).Methods:A total of 6 022 check-ups at the health management center of the Second Hospital of Dalian Medical University from January 2022 to March 2023 were selected as study subjects. MAFLD was categorised into three subtypes: overweight/obese type, metabolic disorder type, and diabetic type. Factor analysis was used to extract dietary patterns. Logistic regression was used to assess the impact of dietary patterns on MAFLD occurrence, constructing interaction models between dietary patterns intake and age, gender, and physical exercise levels. Results:Four dietary patterns were extracted based on feature sorting after factor analysis and were named as the high-quality protein pattern, the fruit-vegetable pattern, egg-aquatic pattern, and the processed meat pattern. Regression analysis of the unadjusted model showed that overweight/obese and diabetic types of MAFLD were negatively associated with the high-quality protein mode, while model-adjusted regression analysis showed that the processed meat pattern was positively associated with the risk of MAFLD, and fruit-vegetable pattern was positively associated with overweight/obese MAFLD( P<0.05). The results of subgroup analyses suggested that female( OR=1.55, 95% CI 1.14-2.15) with a high intake of pickle pattern had a higher risk of overweight/obese MAFLD than male( OR=1.18, 95% CI 1.02-1.49). Conclusion:High-quality protein pattern was negatively correlated with MAFLD, whereas fruit-vegetable pattern and processed meat pattern were positively correlated with MAFLD. Female with high consumption of processed meat pattern are more likely to develop overweight/obesity MAFLD compared with male. It is recommended that people with MAFLD reduce their intake of processed products and high-fructose food, and consume adequate amounts of high-quality protein food to maintain a balanced diet.

Objective:To investigate the relationship between obesity and incident chronic kidney disease(CKD) in a population undergoing health check-ups.Methods:This is a retrospective cohort study. A total of 31 251 participants who had at least 2 health physical examinations in the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2017 to December 2022 and met the inclusion criteria were selected. The participants were divided into normal body weight group, overweight group, and obese group according to baseline body mass index. Cox proportional hazard regression model was used to analyze the relationship between obesity and new-onset CKD, and the dose-response relationship between body mass index and CKD was analyzed with restricted cubic splines.Results:Multivariate Cox regression analysis showed that the risk of developing CKD increased by 13%( HR=1.13, 95% CI 1.01-1.25) and 55%( HR=1.55, 95% CI 1.36-1.76) in the overweight and obese group compared to the normal weight group. Subgroup analysis indicated that obese women had a higher risk of developing CKD compared to men. There was a " U-shaped" correlation between body mass index and CKD in male population, with the lowest risk of CKD occurring at body mass index of 19.6-24.2 kg/m 2. In women, the relationship between body mass index and CKD was approximately linear, with the risk of CKD gradually increasing when body mass index exceeded 22.5 kg/m 2. Conclusions:Obesity is an independent risk factor for new-onset CKD, and obese women have a higher risk of developing CKD than men. Regarding CKD prevention, men are advised to maintain a higher level of body weight within the normal range of body mass index, while women are encouraged to control their weight to a lower level within the normal body mass index range.

Objective:To explore the correlation between systemic immunity-inflammation index(SII) and hyperuricemia(HUA).Methods:Participants who had at least 3 health checkups in the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2022 were selected to construct a dynamic cohort. The SII, reflecting the inflammatory state of the body, was constructed using neutrophil, platelet, and lymphocyte counts. A Cox proportional hazard regression model was used to explore the association between SII and HUA in the overall population and different subgroups of the population, and sensitivity analysis was performed twice. Results:A total of 20 022 subjects were included, and the mean follow-up time was 3.67 years. After adjusting for confounding factors, each unit increase in the natural logarithm of SII(lnSII) was associated with a 24% increased risk of hyperuricemia( HR=1.24, 95% CI 1.16-1.32, P<0.001). As a categorical variable, compared with the lowest quartile array( Q1), the risk of HUA in the total population increased by 12%( HR=1.12, 95% CI 1.03-1.21, P=0.006), 14%( HR=1.14, 95% CI 1.06-1.24, P=0.001), 27%( HR=1.27, 95% CI 1.17-1.37, P<0.001) in Q2, Q3 and Q4 groups within the general population, respectively. All subgroup analysis and sensitivity analysis showed that SII was positively correlated with HUA. Conclusions:Elevated levels of SII significantly increase the risk of HUA. Assessing the body′s inflammatory status using SII can aid in risk screening and preventive management for individuals at high risk of HUA.

Objective:To analyze the laboratory test results of two outbreaks of neonatal enterovirus infections in Guangdong Province in 2019 and the genetic characteristics of Echo11, aiming to provide reference for the prevention and control of neonatal enterovirus infections.Methods:The pathogenic specimens of neonatal cases suspected of enterovirus infection were collected. Fluorescence quantitative PCR and sequencing were used for enterovirus typing and identification, and virus isolation was carried out for positive specimens.The complete sequences of VP1 of Echo11 were amplified and sequenced and the phylogenetic analysis was performed using the bioinformatics software such as Danstar6, Bioedit7.09 and MEGA6.06.Results:A total of 93 specimens from 36 neonatal cases were collected. After identification, 55 specimens from 24 cases were positive for enterovirus, of which 23 cases were positive for Echo11 and one case was positive for Coxsackievirus B4(CVB4). A total of 29 enterovirus strains were isolated from the specimens of 19 cases, of which 28 were Echo11 from 18 cases, and one was CVB4. Phylogenetic analysis revealed that the nucleotide homology between the 18 strains of Echo11 in this study was 98.2%-100.0%, and the nucleotide homology between the Echo11 strains causing the two neonatal infections was 99.7%-100.0% and 99.8%-100.0%, respectively. Echo11 could be divided into six genotypes as A, B, C, D, E and F, in which genotype A and genotype C were further divided into A1-5 and C1-4, and genotype D could be divided into D1-5. The 18 strains of Echo11 in this study were all subtype D5.Conclusions:In 2019, two outbreaks of neonatal infections in medical institutions in Guangdong Province were caused by Echo11, which belonged to the genotype D5.

The interaction between breast cancer cells and the tumor microenvironment plays a vital role in the occurrence and development of breast cancer. Cancer-associated fibroblasts are heterogeneous stromal cells that are abundant in the tumor microenvironment. They participate in tumor angiogenesis, treatment resistance and distant metastasis by secreting a variety of cytokines, growth factors and chemokines. It has great potential as a biomarker for targeted therapy and clinical prognosis of breast cancer, and it can also provide new ideas for adjuvant treatment of breast cancer.

Objective The purpose of this paper is to study the problems and challenges faced by the university in the management of science funds,in order to provide evidence for improving the management level of science funds at university,at the same time to provide possible reference for other universities.Methods Through retrospective analysis of the application and funding of the National Natural Science Foundation of China of Chongqing Medical University during the period of 2013 to 2018.Results In recent years,the funding level of science funds in our university has entered the bottleneck period,main influencing factors include policy,talents,management and so on.Conclusions It is suggested to strengthen the top-level design of scientific research management,strengthen the training of scientific research personnel,and ensure the capability of scientific research management.