Objective To investigate the epidemiological characteristics of cross-county imported dengue fever cases within Yunnan province in 2023, so as to provide insights into formulation of preventive and control measures for intra-provincial spread of dengue fever. Methods All data pertaining cross-county imported dengue fever cases within Yunnan Province in 2023 were collected, and the temporal, regional and population distributions of the cases were descriptively analyzed. Results A total of 1 664 intra-provincial cross-county imported dengue fever cases were reported in 95 counties (cities, districts) cross 16 profectures (cities) in Yunnan Province in 2023, accounting for 12.34% of total cases in the province. Cross-county imported dengue fever cases were predominantly reported during the period between August and October (1 516 cases, 91.11% of total cases), and peaked in September (659 cases), with a single-day peak on October 8 (36 cases). During the period from September 4 to 10, five counties (cities) with local dengue fever epidemics, including Jinghong City of Xishuangbanna Dai Autonomous Prefecture, Gengma Dai and Wa Autonomous County of Lincang City, Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, Mengla Coun ty of Xishuangbanna Dai Autonomous Prefecture, and Zhenkang County of Lincang City, exported 165 cross-county imported dengue fever cases to the rest of the province. Among the 1 644 intra-provincial cross-county imported dengue fever cases, the male to female ratio was 1.40∶1.00, and 1 329 cases were at ages of 15 to 55 years (79.87%), with farmers as the predominant occupation (886 cases, 53.25%). The top 5 counties (cities/districts) reporting the highest number of intra-provincial cross-county imported dengue fever cases included Simao District (266 cases) and Lancang Lahu Autonomous County (118 cases) of Pu’er City, Mengla County (91 cases) and Menghai County (91 cases) of Xishuangbanna Dai Autonomous Prefecture, and Mangshi City (73 cases) of Dehong Dai and Jingpo Autonomous Prefecture, which accounting for 38.40% of total imported cases. These intra-provincial cross-county imported dengue fever cases originated from 7 counties (cities/districts) in 4 prefectures (cities), including 1 261 cases (76.70%) from Jinghong City of Xishuangbanna Dai Autonomous Prefecture, 224 cases (13.63%) from Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, 103 cases (6.27%) from Gengma Dai and Wa Autonomous County of Lincang City, 31 cases (1.89%) from Mengla County of Xishuangbanna Dai Autonomous Prefecture, 30 cases (1.82%) from Zhenkang County of Lincang City, 10 cases (0.61%) from Cangyuan Wa Autonomous County of Lincang City, and 5 cases (0.30%) from Mohan-Boten Economic Cooperation Zone of Kunming City. In addition, local dengue fever epidemics following intra-provincial cross-county importation of dengue fevers cases in Simao District, Jinggu Dai and Yi Autonomous County, Mangshi City, Longchuan County, and Cangyuan Wa Autonomous County. Conclusions Farmers and students are high-risk populations for intra-provincial cross-county imported dengue fever cases in Yunnan Province, and health education pertaining personal protection against dengue fever should be strengthened among these high-risk populations by governments at all levels. There is a high risk of local out-break of dengue fever following continuous introduction of intra-provincial cross-county imported cases. Standardized management of intra-provincial cross-county imported dengue fever cases should be reinforced to reduce the risk of local epidemics.

This study aims to integrate data mining techniques of single cell transcriptomics and spatial transcriptomics, along with animal experiment validation, so as to systematically characterize the protective effects of Jianpi Tongluo Formula(JTF) on the cartilage in knee osteoarthritis(KOA) and elucidate the underlying molecular mechanisms. Single cell transcriptomics and spatial transcriptomics datasets(GSE254844 and GSE255460) of the cartilage tissue obtained from KOA patients were analyzed to map the single cell-spatial heterogeneity and identify key pathogenic factors. After that, a KOA rat model was established via knee joint injection of papain. The intervention effects of JTF on the expression features of these key factors were assessed through real-time quantitative polymerase chain reaction(PCR), Western blot, and immunohistochemical staining. As a result, the integrated single cell and spatial transcriptomics data identified distinct cell subsets with different pathological changes in different regions of the inflamed cartilage tissue in KOA, and their differentiation trajectories were closely related to the inflammatory fibrosis-like pathological changes of chondrocytes. Accordingly, the expression levels of the two key effect targets, namely nuclear receptor coactivator 4(NCOA4) and high mobility group box 1(HMGB1) were significantly reduced in the articular surface and superficial zone of the inflamed joints when JTF effectively alleviated various pathological changes in KOA rats, thus reversing the abnormal chondrocyte autophagy level, relieving the inflammatory responses and fibrosis-like pathological changes, and promoting the repair of chondrocyte function. Collectively, this study revealed the heterogeneous characteristics and dynamic changes of inflamed cartilage tissue in different regions and different cell subsets in KOA patients. It is worth noting that NCOA4 and HMGB1 were crucial in regulating chondrocyte autophagy and inflammatory reaction, while JTF could reverse the regulation of NCOA4 and HMGB1 and correct the abnormal molecular signal axis in the target cells of the inflamed joints. The research can provide a new research idea and scientific basis for developing a personalized therapeutic schedule targeting the spatiotemporal heterogeneity characteristics of KOA.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Osteoarthritis, Knee/pathology* ; Humans ; Male ; Cartilage, Articular/metabolism* ; Chondrocytes/metabolism* ; Rats, Sprague-Dawley ; Female ; Protective Agents/administration & dosage* ; Single-Cell Analysis ; Middle Aged ; HMGB1 Protein/metabolism*

PURPOSE: To assess the relationship between dislocation and functional outcomes in supination-external rotation (SER) ankle fractures. METHODS: A retrospective case series study was performed on patients with ankle fractures treated surgically at a large trauma center from January 2015 to December 2021. The inclusion criteria were young and middle-aged patients of 18 - 65 years with SER ankle fractures that can be classified by Lauge-Hansen classification and underwent surgery at our trauma center. Exclusion criteria were serious life-threatening diseases, open fractures, fractures delayed for more than 3 weeks, fracture sites ≥ 2, etc. Then patients were divided into dislocation and no-dislocation groups. Patient demographics, injury characteristics, surgery-related outcomes, and postoperative functional outcomes were collected and analyzed. The functional outcomes of SER ankle fractures were assessed postoperatively at 1-year face-to-face follow-up using the foot and ankle outcome score (FAOS) and American Orthopedic Foot and Ankle Society ankle hindfoot score and by 2 experienced orthopedic physicians. Relevant data were analyzed using SPSS version 22.0 by Chi-square or t-test. RESULTS: During the study period, there were 371 ankle fractures. Among them, 190 (51.2%) were SER patterns with 69 (36.3%) combined with dislocations. Compared with the no-dislocation group, the dislocation group showed no statistically significant differences in gender, age composition, fracture type, diabetes, or smoking history, preoperative waiting time, operation time, and length of hospital stay (all p > 0.05), but a significantly higher Lauge-Hansen injury grade (p < 0.001) and syndesmotic screw fixation rate (p = 0.033). Moreover, the functional recovery was poorer, revealing a significantly lower FAOS in the sport/rec scale (p < 0.001). Subgroup analysis showed that among SER IV ankle fracture patients, FAOS was much lower in pain (p = 0.042) and sport/rec scales (p < 0.001) for those with dislocations. American Orthopedic Foot and Ankle Society ankle hindfoot score revealed no significant difference between dislocation and no-dislocation patients. CONCLUSION Dislocation in SER ankle fractures suggests more severe injury and negatively affects functional recovery, mainly manifested as more pain and poorer motor function, especially in SER IV ankle cases.
Humans ; Ankle Fractures/physiopathology* ; Male ; Female ; Retrospective Studies ; Adult ; Middle Aged ; Supination ; Aged ; Young Adult ; Rotation ; Joint Dislocations/surgery* ; Fracture Fixation, Internal/methods* ; Adolescent ; Recovery of Function ; Treatment Outcome

OBJECTIVES: To explore the mediating role of sleep duration in the relationship between depression symptoms and myopia among middle school students. METHODS: This study was a cross-sectional research conducted using a stratified cluster random sampling method. A total of 1 728 middle school students were selected from two junior high schools and two senior high schools in certain urban areas and farms of the Xinjiang Production and Construction Corps. Questionnaire surveys and vision tests were conducted among the students. Spearman analysis was used to analyze the correlation between depression symptoms, sleep duration, and myopia. The Bootstrap method was employed to investigate the mediating effect of sleep duration between depression symptoms and myopia. RESULTS: The prevalence of myopia in the overall population was 74.02% (1 279/1 728), with an average sleep duration of (7.6±1.0) hours. The rate of insufficient sleep was 83.62% (1 445/1 728), and the proportion of students exhibiting depression symptoms was 25.29% (437/1 728). Correlation analysis showed significant negative correlations between visual acuity in both eyes and sleep duration with depressive emotions as measured by the Center for Epidemiologic Studies Depression Scale (with correlation coefficients of -0.064, -0.084, and -0.199 respectively; P<0.01), as well as with somatic symptoms and activities (with correlation coefficients of -0.104, -0.124, and -0.233 respectively; P<0.01) and interpersonal relationships (with correlation coefficients of -0.052, -0.059, and -0.071 respectively; P<0.05). The correlation coefficients for left and right eye visual acuity and sleep duration were 0.206 and 0.211 respectively (P<0.001). Sleep duration exhibited a mediating effect between depression symptoms and myopia (indirect effect=0.056, 95%CI: 0.029-0.088), with the mediating effect value for females (indirect effect=0.066, 95%CI: 0.024-0.119) being higher than that for males (indirect effect=0.042, 95%CI: 0.011-0.081). CONCLUSIONS Sleep duration serves as a partial mediator between depression symptoms and myopia in middle school students.
Humans ; Myopia/etiology* ; Male ; Female ; Depression/physiopathology* ; Cross-Sectional Studies ; Sleep ; Adolescent ; Students ; Child ; Time Factors ; Sleep Duration

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.