Objective To systematically evaluate the risk prediction models of diabetic foot infections,and to ex-plore its implications for clinical nursing care.Methods Relevant studies in the databases of CNKI,VIP,Wanfang,CBM,CINAHL,PubMed,Web of Science,Cochrane Library,Embase,ProQuest,and Ovid were searched for the time-frame of database construction to December 2023.Literature was screened independently by 2 investigators and the area under the model curve(AUC)and its 95％confidence interval(CI),calibration method and predictors were ex-tracted,and the quality of the model was evaluated using the Predictive Modeling Study Risk of Bias Assessment Tool.Meta-analysis of the predictive value of model predictors was performed using RevMan 5.4 software.Results 7 studies were included,containing 8 risk prediction models,all of which reported the AUC(0.748-0.922),and 6 models underwent model calibration.The overall fit of the included studies was good,among which l model was low fit,and all 7 studies were at high risk of bias,mainly focusing on predictors and analysis.Meta-analysis showed that the area under the curve of the combined models was 0.831(95％CI:0.780～0.883);the disease duration(OR=2.460,95％CI:1.850～3.270),vascular disease(OR=3.110,95％CI:1.070～9.000),and neuropathy(OR=3.550,95％CI:1.920～6.560)were effective predictors of diabetic foot infection.Conclusion The current diabetic foot infection risk prediction model is still in the developmental stage,and further optimization and external validation of the ex-isting model is recommended for the future.

Colorectal cancer （CRC） is one of the refractory malignant tumors of the digestive system worldwide. With limitations， the available clinical therapies are usually suspended or show unsatisfactory effect. Therefore， it is urgent to find and develop new candidate drugs specifically targeting the cancer with ideal efficacy， low toxicity， and low cost， and the solutions can be found in traditional Chinese medicine （TCM） which has a long history. In TCM， sovereign， ministry， assistant， and guiding medicinals are selected based on the syndrome differentiation， and it has shown remarkable efficacy on CRC in recent years. In particular， Chinese medicinal compounds and monomers from Chinese medicinals which have been applied in clinical settings are advantageous in the treatment of CRCs， as they improve the quality of life， alleviate clinical symptoms and toxic and side effects of chemotherapy， and prolong the survival of patients. Therefore， we retrieved the English and Chinese articles with "CRC"， "TCM"， "compound" and "monomer" as keywords， and summarized the progress in the treatment of CRC with Chinese medicinal compounds and monomers from Chinese medicinals from four aspects of "replenishing Qi and invigorating spleen"， "clearing heat and removing toxin"， "nourishing liver and kidney"， and "tonifying Qi and nourishing blood". However， Chinese medicine features multiple components， multiple targets， and multiple pathways， and in-depth research should be carried out on the application of Chinese medicinal compounds and monomers from Chinese medicinals in the treatment of CRC， in an attempt to minimize the pain and side effects and maximize the therapeutic effect. This study is expected to provide new insight into the treatment of CRC and a reference for further research on the efficacy and mechanism of Chinese medicine.

Objective:To investigate the role of serum hepatitis B virus RNA (HBV RNA) in predicting HBeAg serological conversion in children with chronic hepatitis B.Methods:175 children aged 1~17 years with chronic hepatitis B who received interferon α (IFNα) for 48 weeks were selected. Patients were divided into HBeAg seroconversion and non-conversion based on whether HBeAg seroconversion occurred at 48 weeks of treatment.T-test and Mann-Whitney U test were used to compare between groups; chisquare test or Fisher exact probability method was used to compare the frequency between groups of classified variables; and Pearson correlation was used to analyze the correlation between indicators. Univariate and multivariate logistic regression analyses were used to identify influencing factors associated with HBeAg serological conversion. The predictive effect of HBV RNA, HBV DNA, and HBsAg on HBeAg serological conversion was compared and analyzed by the receiver operating characteristic curve (ROC).Results:The seroconversion rate of HBeAg at 48 weeks was 36.0% (63/175). The reduction in HBVRNA levels from baseline to the 12th, 24th, 36th, and 48th weeks of antiviral therapy was significantly greater in the HBeAg serological conversion group than that in the non-conversion group, and the difference was statistically significant between the two groups (P < 0.05). Univariate and multivariate regression analyses showed that age and a decline in HBV RNA levels at week 12 were independent predictors of HBeAg serological conversion. The area under the ROC curve (AUROC) of HBV RNA decline at week 12 was 0.677(95% CI∶0.549-0.806, P = 0.012), which was significantly better than the same period of AUROC of HBV DNA (0.657, 95% CI∶0.527-0.788, P = 0.025) and HBsAg (0.660, 95% CI∶0.526-0.795, P = 0.023) decline. HBV RNA levels decreased (>1.385 log10 copies/ml) at week 12, with a positive predictive value of 53.2%, a negative predictive value of 72.2%, a sensitivity of 77.4%, and a specificity of 57.9% for HBeAg seroconversion. Conclusion:HBV RNA level lowering during the 12th week of antiviral therapy can serve as an early predictor marker for HBeAg serological conversion in children with chronic hepatitis B.

Objective:To analyze the incidence and epidemiological characteristics of traumatic spinal cord injury in China in 2018.Methods:Multi-stage stratified cluster sampling was used to randomly select hospitals capable of treating patients with spinal cord injury from 3 regions，9 provinces and 27 cities in China to retrospectively investigate eligible patients with traumatic spinal cord injury admitted in 2018. National and regional incidence rates were calculated. The data of cause of injury，injury level，severity of injury，segment and type of fracture，complications，death and other data were collected by medical record questionnaire，and analyzed according to geographical region，age and gender.Results:Medical records of 4，134 patients were included in this study，with a male-to-female ratio of 2.99∶1. The incidence of traumatic spinal cord injury in China in 2018 was 50.484 / 1 million （95% CI 50.122-50.846）. The highest incidence in the Eastern region was 53.791 / 1 million （95% CI 53.217-54.365）. In the whole country，the main causes of injury were high falls （29.58%），as well as in the Western region （40.68%），while the main causes of injury in the Eastern and Central regions were traffic injuries （31.22%，30.10%）. The main injury level was cervical spinal cord in the whole country （64.49%），and the proportion of cervical spinal cord injury in the Central region was the highest （74.68%），and the proportion of lumbosacral spinal cord injury in the Western region was the highest （32.30%）. The highest proportion of degree of injury was incomplete quadriplegia （55.20%），and the distribution pattern was the same in each region. A total of 65.87% of the patients were complicated with fracture or dislocation，77.95% in the Western region and only 54.77% in the Central region. In the whole country，the head was the main combined injury （37.87%），as well as in the Eastern and Central regions，while the proportion of chest combined injury in the Western region was the highest （38.57%）. A total of 32.90% of the patients were complicated with respiratory complications. There were 23 patients （0.56%） died in hospital，of which 17（73.91%） died of respiratory dysfunction. Conclusions:The Eastern region of China has a high incidence of traumatic spinal cord injury. Other epidemiological features include high fall as the main cause of injury cervical spinal cord injury as the main injury level，incomplete quadriplegia as the main degree of injury，head as the main combined injury，and respiratory complications as the main complication.

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
Artificial Intelligence ; Diabetes Mellitus ; Diabetic Foot ; Humans

Objective: To explore the effect of ambient fine particulate matters (PM_2.5) and temperature interaction on schizophrenia admission. Methods: All admission data were retrieved from the Psychiatric Hospital and Municipal Hospital of TongLing from January 1, 2014 to December 31, 2017. Daily air pollution and meteorological data were collected from the Tongling Environmental Protection Agency and Meteorological Bureau, respectively. A distributed lag non-linear model combined with the generalized additive model were applied to explore the effects of PM_2.5, multi-pollutants, and the interaction between temperature and PM_2.5 on schizophrenia admission. The stratification of temperature was divided by three criteria. The low temperature layer was defined as <P₅ or <P₁₀ or <P₂₀; P₅-P₉₅ or P₁₀-P₉₀ or P₂₀-P₈₀ was defined as the middle temperature layer; >P₉₅ or >P₉₀ or >P₈₀ was defined as the high temperature layer. Results: From 2014 to 2017, 6 642 patients were admitted for schizophrenia in Tongling, and the median of PM_2.5 and temperature were 47.0 μg/m³ and 17.5 ℃, respectively. The median concentration of PM_2.5 (P₅₀) was taken as a reference. When the exposure concentration of PM_2.5 was P₉₀, the lagged effect appeared in the first day with RR=1.03 (95%CI: 1.00-1.07) and reached the maximum in the fifth day with RR=1.16 (95%CI: 1.13-1.19). In the multi-pollutant models, it was found that the simultaneous inclusion of PM_2.5 and NO₂ had higher risk of schizophrenia admission, with the RR=1.18 (95%CI: 1.15-1.22), P<0.001. The risk of schizophrenia admission caused by PM_2.5 exposure at high temperature was greatest under the three temperature stratification standards, which were 12.1% (8.5%-15.7%), 9.7% (6.9%-12.6%) and 17.1% (11.6%-22.8%), all P values <0.001. Conclusion With the increase of PM_2.5 concentration, the risk of schizophrenia admission is increased, and the risk effect of PM_2.5 is stronger at high temperature.

The concept and significance of readiness for hospital discharge, measuring tools, application status, shortcomings and prospects of readiness for hospital discharge among patients with chronic diseases were reviewed in this article, with attempts to draw more attention to the readiness for hospital discharge among patients with chronic diseases in clinical practice and provide theoretical basis for forming the intervention plan suitable for China.

Objective? To construct the "Internet +" based health management model for physical examination population. Methods? From September 2016 to September 2017, we constructed the primary"Internet +" health management model by literature review. Two rounds of consultation with 25 experts were carried out with the Delphi method to construct the "Internet +" health management model for physical examination population. Results? Among two rounds of consultation, the recovery rate of questionnaire was all 100%. The expert authority coefficient (Cr) was 0.87. The final "Internet +" health management model included 3 first-level indicators, 7 second-level indicators and 26 third-level indicators. Conclusions? The "Internet +"based health management model is reliable which can be intervention for health management model of physical examination population.

Objective To understand the hospital discharge readiness and discharge instruction quality in chronic disease patients and to analyze the correlation between them. Methods Totals of 602 chronic disease patients of related departments from 7 ClassⅢGrade A hospitals in Shanghai were selected by convenience sampling. All of them were investigated with the general information questionnaire, Readiness for Hospital Discharge Scale (RHDS) and Quality of Discharge Teaching Scale (QDTS), and their data were analyzed. Results Among those patients, the scores of hospital discharge readiness and discharge instruction quality were (8.01±1.41) and (8.65±1.29) out of 10 respectively. The score of hospital discharge readiness had a positive correlation with the score of discharge instruction quality with a statistical difference (r=0.507, P＜ 0.01). Conclusions Hospital discharge readiness and discharge instruction quality of chronic disease patients are all in high levels and with a positive correlation. Nurses should enrich the content of discharge instruction to improve the quality of discharge instruction by appropriate instruction skills and to improve the hospital discharge readiness.

Objective To investigate the differences of gut microbiota in patients with schizophrenia between onset and remission. Methods Twelve patients with schizophrenia in the stage of onset were selected as the episode group. Thirteen gender-, age-and BMI-matched patients with schizophrenia during the remission period were selected as the control group. The fecal specimens of the two groups were collected for high-throughput sequencing of the 16S rRNA gene, and the gut microbiota differences between the two groups were analyzed. Results The chao index and ace index of gut microbiota was lower in the episode group than in the control group (t=2.385,P=0.026; t=3.068,P=0.005). The relative abundance of Bacteroides was higher and the relative abundance of Prevotella was lower in the episodes group than in the control group (Z=-2.013, P =0.044; Z=-3.427, P=0.001). The relative abundance of the Prevotella was negatively correlated with the PANSS score (r=-0.577, P=0.003). Conclusion Schizophrenia in the stage of acute episode have altered gut microbiota compared with those in the stage of remission and the gut microbiota is significantly corrected with mental symptoms.