Background: Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. Methods: Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. Results: Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. Conclusion Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.

Background: Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. Methods: Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. Results: Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. Conclusion Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.

ObjectiveTo investigate the predictive value of heart rate turbulence(HRT) in patients with diabetes mellitus (DM) after acute myocardial infarction (AMI).MethodsNinety-two AMI patients combined with DM (DM group) and 120 AMI patients without DM (non-DM group) were selected.Turbulence onset (TO) and turbulence slope (TS) were two indexes of HRT.HRT was considered positive when TO was ≥0 and TS was ≤2.5 ms/R-R.The differences in clinical data between HRT-positive and HRT-negative patients were compared.And the related risk factors after AMI were analyzed.ResultsAge,left ventricular ejection fraction (LVEF) level,renal insufficiency,LVEF＜40％,standard deviation of sinus cardiac cycle (R-R interval)(SDNN),heart rate variability (HRV) positiveand HRT indexes (TO,TS) between HRT-positive and HRT-negative patients in DM group had significant differences (P ＜ 0.05 ).Age,LVEF level,SDNN and HRT indexes(TO,TS) between HRT-positive and HRT-negative patients in non-DM group had significant differences(P ＜ 0.05).Multivariate Cox regression analysis showed that renal insufficiency (OR=4.8,95％ CI:1.8 - 10.7,P=0.008) and HRT positive (OR=3.7,95％ CI:1.5 - 8.6,P=0.070) in DM group had statistical significance.And HRT positive in non-DM group had statisticalsignificance(OR=23.0,95％ CI:5.2 ～ 86.0,P ＜ 0.01 ).ConclusionsHRT,an index of dynamic electrocardiogram,can predict the risk in patients with DM or without DM after AMI.

Objective To explore the CT features of pulmonary lymphangiomyomatosis (PLAM).Methods Clinical and high resolution CT (HRCT)data of 14 patients with pathologically proved PLAM were analyzed retrospectively.The clinical and CT features were summarized by combining the literatures.Results All 14 cases were female.They all presented with dyspnea in different degree after the activity. Scattered or widely distributed translucent and cystic lesions with indistinct walls in bilateral lungs were seen on routine CT images. HRCT showed homogeneous clear thin-walled cysts with diameter ranging from several millimeters to 25 mm,wall thickness of 1-2 mm,and surrounded by normal lung tissue.Meanwhile,blood vessels were found around the cysts,and there were no central lobular cores.The cysts were different sizes and irregular distribution.6 patients had extra-pulmonary CT manifestations:1 case with intracranial multiple sclerosis, hepatic and renal angiomyolipomas,and hepatic multiple hemangiomas,3 cases with mediastinal,hepatic and renal angiomyolipomas, and 2 cases with retroperitonea lymphangioleiomyomatosis.Conclusion The CT of PLAM is characterized by the diffuse distribution of thin-walled cystic cavities and the wall thickness is generally uniform.The typical manifestations of HRCT combined with clinical data have great values in the early diagnosis and differential diagnosis.

Objective: To investigate the status of insomnia, Internet addiction, and depressive symptoms among medical students and to analyze the effect of Internet addiction on insomnia and the mediating role of depressive symptoms, in order to provide a basis for the development of targeted interventions and measurements for medical students. Methods: A stratified whole group sampling method was used to select full-time college students from three medical universities in Anhui Province. The Chinese version of Insomnia Severity Index (ISI), Internet Addiction Test (IAT) scale and 9-item Patient Health Questionnaire (PHQ-9) were used to evaluate the symptoms of insomnia, Internet addiction and depressive in students. A multivariate Logistic regression analysis was used to explore the factors influencing insomnia among medical students and to analyze the relationship between insomnia with Internet addiction and depressive symptoms, respectively. Results: The overall rate of Internet addiction was 49.5%, depressive symptoms was 39.5%, insomnia was 18.6%. High academic stress, and the presence of surrounding people diagnosed with COVID-19 were associated with a higher risk of insomnia ( P <0.05). The higher the level of Internet addiction (mild, OR =2.60; moderate/severe, OR =4.21) and depression. (mild, OR =6.35; moderate/severe, OR =19.32), the higher the risk of insomnia. Mediated effect analysis showed that Internet addiction had a direct predictive effect ( β =0.02, P <0.05) on insomnia and also indirectly affected insomnia through depression (indirect effect=0.07，95% CI =0.06-0.08). Conclusion The detected rates of insomnia, Internet addiction and depressive symptoms are high among medical students in Anhui Province, and Internet addiction and depressive symptoms are risk factors for insomnia, which should be given more attention and appropriate interventions when necessary to improve their physical and mental health.

Objective:To construct a clinical prediction model for early postoperative parastomal hernia in patients with enterostomy and to conduct internal and external validation and evaluation of clinical benefit.Methods:A total of 1 071 patients with enterostomy treated in Zhongshan Hospital Affiliated to Fudan University from October 2013 to December 2020 were selected as the research objects by the convenient sampling method. Patients from October 2013 to December 2019 were selected as the modeling group ( n=943) . The data of patients were obtained by questionnaire and other methods. The clinical prediction model of early postoperative parastomal hernia in patients with enterostomy was constructed based on Cox regression analysis and the model was internally verified by Bootstrap method. Patients from January to December 2020 were selected as the validation group ( n=128) for external validation of the model. C-statistic, area under receiver operating characteristic curve and calibration map were used to evaluate the discrimination and calibration of the model. Decision curve analysis was used to draw decision curve analysis chart to evaluate the clinical benefit of the prediction model. Results:Age, history of alcohol consumption, postoperative body mass index, diabetes mellitus, respiratory diseases, history of abdominal surgery, stoma route, stoma nature and C-reactive protein were independent influencing factors for early postoperative parastomal hernia in patients with enterostomy ( P<0.05) . The C- index value of early postoperative nomogram was 0.710 (95% CI: 0.660-0.750) . Conclusions:The clinical prediction model of early parastomal hernia in patients with enterostomy has good predictive performance, which can help clinical medical staff to screen out high-risk groups in time and guide medical staff to focus on prevention.

Objective:To construct a clinical prediction model for early postoperative parastomal hernia in patients with enterostomy and to conduct internal and external validation and evaluation of clinical benefit.Methods:A total of 1 071 patients with enterostomy treated in Zhongshan Hospital Affiliated to Fudan University from October 2013 to December 2020 were selected as the research objects by the convenient sampling method. Patients from October 2013 to December 2019 were selected as the modeling group ( n=943) . The data of patients were obtained by questionnaire and other methods. The clinical prediction model of early postoperative parastomal hernia in patients with enterostomy was constructed based on Cox regression analysis and the model was internally verified by Bootstrap method. Patients from January to December 2020 were selected as the validation group ( n=128) for external validation of the model. C-statistic, area under receiver operating characteristic curve and calibration map were used to evaluate the discrimination and calibration of the model. Decision curve analysis was used to draw decision curve analysis chart to evaluate the clinical benefit of the prediction model. Results:Age, history of alcohol consumption, postoperative body mass index, diabetes mellitus, respiratory diseases, history of abdominal surgery, stoma route, stoma nature and C-reactive protein were independent influencing factors for early postoperative parastomal hernia in patients with enterostomy ( P<0.05) . The C- index value of early postoperative nomogram was 0.710 (95% CI: 0.660-0.750) . Conclusions:The clinical prediction model of early parastomal hernia in patients with enterostomy has good predictive performance, which can help clinical medical staff to screen out high-risk groups in time and guide medical staff to focus on prevention.

Objective To investigate the prevalence of nutritional risk,undernutrition and nutritional support among elderly inpatients with coronary heart disease in 11 tertiary A hospitals in China.Methods Records of elderly patients under the age of 90 with coronary heart disease were collected between March 2012 and May 2012 from 11 tertiary A hospitals in China following the direction of diagnosis related group of Beijing government.Results A total of 1 279 consecutive cases were recruited with the average age 74 years old (65-89).The total nutritional risk prevalence was 28.14％ (360/1 279).The prevalence of nutritional risk and nutritional risk score ≥ 5 increased with age.The prevalence of nutritional risk (12.88％ vs.30.08％ vs.42.28％) and nutritional risk scored ≥5 (10.86％ vs.18.61％ vs.27.78％)increased with age.Judging from BMI,most patients were overweight or obese (BMI ≥ 24 kg/m2),accounting for 53.0％ of the total,and prevalence of nutritional risk in this subgroup was 15.12％ (96/635).The prevalence of nutritional risk in patients with normal BMI was 34.24％.The prevalence of undernutrition defined as BMI＜ 18.5 kg/m2 was 4.25％ (51/1 279),among which patients with score ≥ 5 account for 64.7％ (33/51).The prevalence of undernutrition defined as nutritional impairment score =3 was 7.58％ (97/1 279).In patients with nutritional risk,57 were administrated nutrition support (16.6％);in patients without nutritional risk,21 received nutrition support,mostly parenteral nutrition (16 cases,76.2％).In patients with nutritional risk [(79.46± 7.19) years vs.(76.40± 6.16) years],there were statistically significant difference between those who received nutrition support and those who did not in terms of age and the ratio of patients with nutritional risk scored≥5 (35.1％ vs.17.1％) (P =0.001,P=0.002).Conclusions The prevalence of nutritional risk in patients with coronary heart disease was high.The prevalence of undernutrition was low.Prevalence of overweight and obese was high,but there was still nutritional risk in this group of patients.The patients who received nutrition support were older and had high nutritional impairment score,but the indication is not rationale.