BackgroundAdolescents with depressive disorder often engage in non-suicidal self-injury （NSSI） behaviors， which severely impacts their physical and mental health. Impulsivity and emotional regulation are key factors influencing NSSI behaviors. However， research on the mechanisms through which impulsivity and emotional regulation affect NSSI behaviors in adolescent depressive disorder patients with NSSI remains insufficient， limiting the development of effective intervention strategies. ObjectiveTo explore the differences in impulsivity and emotion regulation abilities between adolescent patients with depressive disorder accompanied by and without NSSI behaviors， and to analyze the association between NSSI behaviors and impulsivity and emotion regulation abilities in adolescent patients with depressive disorder accompanied by NSSI behaviors. MethodsA total of 184 adolescents hospitalized in the child and adolescent psychiatry department of Wuxi Mental Health Center from October 2023 to August 2024， who met the diagnostic criteria for depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）， were consecutively enrolled as study subjects. Based on the diagnostic criteria for NSSI in DSM-5， patients were divided into NSSI group （n=108） and non-NSSI group （n=76）. The Barratt Impulsiveness Scale-11 （BIS-11）， the Emotion Regulation Questionnaire （ERQ）， the Adolescent Self-Harm Questionnaire， and the Positive and Negative Suicide Ideation （PANSI） were used for assessment. Spearman correlation analysis was employed to explore the correlation between the scores of the Adolescent Self-Harm Questionnaire and the scores of BIS-11 and ERQ in the NSSI group. Multiple regression analysis was conducted to examine the effects of impulsivity and emotion regulation on NSSI behaviors in the NSSI group. ResultsCompared to the non-NSSI group， the NSSI group showed significantly higher scores in BIS-11 non-planned impulsivity （Z=-4.181， P<0.05）， action impulsivity （t=4.944， P<0.05）， cognitive impulsivity （Z=-3.392， P<0.05）， and total score （t=4.763， P<0.05）， and lower scores in the cognitive reappraisal of ERQ （t=-4.094， P<0.05） and total score （Z=-2.299， P<0.05）， and higher scores in the expression inhibition of ERQ （Z=-3.019， P<0.05）. The correlation analysis results showed that the score of the adolescent self-harm questionnaire in the NSSI group was positively correlated with the behavioral impulsivity factor score in the BIS-11 （r=0.434， P<0.05）. Multiple regression analysis indicated that action impulsivity factor was a significant correlate of self-injury behaviors in the NSSI group （B=0.855， P<0.05）， explaining 22.30% of the total variance. ConclusionAdolescent patients with depressive disorder accompanied by NSSI behaviors exhibit higher levels of impulsivity and poorer emotional regulation abilities. Action impulsivity may play a significant role in the mechanism of NSSI behaviors. ［Funded by Wuxi Municipal Health Commission Research Project （number， Q202320）］

Objective: To explore relationship of sleep quality with overweight and obesity among middle school students, so as to provide a reference basis for improving adolescent sleep health. Methods: From September to December 2023, 5 713 middle school students aged 13 to 18 were selected by stratified cluster random sampling method in six regions, including Shanghai, Suzhou, Taiyuan, Wuyuan, Xingyi and Urumqi. Sleep quality survey was conducted on middle school students by Pittsburgh Sleep Quality Index. Height and weight were measured, and World Health Organization s standards for growth and development of children and adolescents was used to evaluate their nutritional status. Both χ 2 test and Logistic regression analysis were used to analyze the association between sleep quality and nutritional status of middle school students. Results: The non compliance detection rate of sleep quality was 38.4% among girls, but 29.2% among boys, and the difference was of statistical significance( χ 2=54.08, P < 0.01 ). The detection rate of substandard sleep quality was 34.2% in the group with normal nutritional status, 38.3% in the group with overweight, 43.7% in the group with obesity and 26.0% in the group with emaciation, and the difference in the rates of substandard sleep quality among middle school students of different nutritional status was statistically significant ( χ 2=68.15, P <0.01). Logistic regression analysis showed that, after controlling for mental health and physical activity, the detection rate of substandard sleep quality in the obese groups was 1.30 times higher than that in the normal group, respectively( OR =1.30, 95% CI =1.06- 1.59 , P <0.01). Conclusions Sleep quality is correlated with overweight and obesity among middle school students, and there are gender differences. Intervention policies should be formulated according to the characteristics of different genders.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

One of the basic questions in the aging field is whether there is a fundamental difference between the aging of lower invertebrates and mammals. A major difference between the lower invertebrates and mammals is the abundancy of noncoding RNAs, most of which are not conserved. We have previously identified a noncoding RNA Terc-53 that is derived from the RNA component of telomerase Terc. To study its physiological functions, we generated two transgenic mouse models overexpressing the RNA in wild-type and early-aging Terc-/- backgrounds. Terc-53 mice showed age-related cognition decline and shortened life span, even though no developmental defects or physiological abnormality at an early age was observed, indicating its involvement in normal aging of mammals. Subsequent mechanistic study identified hyaluronan-mediated motility receptor (Hmmr) as the main effector of Terc-53. Terc-53 mediates the degradation of Hmmr, leading to an increase of inflammation in the affected tissues, accelerating organismal aging. adeno-associated virus delivered supplementation of Hmmr in the hippocampus reversed the cognition decline in Terc-53 transgenic mice. Neither Terc-53 nor Hmmr has homologs in C. elegans. Neither do arthropods express hyaluronan. These findings demonstrate the complexity of aging in mammals and open new paths for exploring noncoding RNA and Hmmr as means of treating age-related physical debilities and improving healthspan.
Animals ; Mice ; RNA, Untranslated/metabolism* ; Aging/genetics* ; Mice, Transgenic ; Telomerase/metabolism* ; RNA/genetics* ; Hippocampus/metabolism* ; Humans ; Mice, Inbred C57BL

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BACKGROUND:There is still no consensus on the optimal internal fixation for the treatment of Pauwels Ⅲ femoral neck fracture,and most of the related finite element analyses have been performed using a single simplified loading condition,and the biomechanical properties of commonly used internal fixation devices need to be further investigated. OBJECTIVE:To analyze the biomechanical characteristics of Pauwels Ⅲ femoral neck fractures treated with cannulated compression screw,dynamic hip screw,and femoral neck system by finite element method under different loading conditions of single-leg standing loads and sideways fall loads. METHODS:The DICOM data of healthy adult femur were obtained by CT scanning,imported into Mimics 15.0 software to obtain the rough model of bone tissue.The data exported from Mimics were optimized by Geomagics software,and then three internal fixation models were built and assembled with the femur model according to the parameters of the clinical application of the cannulated compression screw,dynamic hip screw,and femoral neck system by using Pro/E software.Finally,the three internal fixation models were imported into Ansys software for loading and calculation to analyze the stress distribution and displacement of the femur and the internal fixation under different working conditions of single-leg standing loads and sideways fall loads,as well as the stress characteristics of the calcar femorale and Ward's triangle. RESULTS AND CONCLUSION:(1)Under the single-leg standing load and the sideways fall load,the proximal femoral stress of the three internal fixation models was mainly distributed above the fracture end of the femoral neck.The peak stress of the proximal femoral end,fracture end,Ward triangle,and calcar femorale of the three internal fixation models were the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(2)Under the single-leg standing load and the sideways fall load,the peak displacement of the proximal femur of the three internal fixation models was all located at the top of the femoral head,and the peak displacement was the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(3)The peak displacement of the three internal fixation models was all located at the top of the internal fixation device under the single-leg standing and sideways fall loading conditions,and the peak displacement values were the smallest in the femoral neck system internal fixation model and the largest in the cannulated compression screw internal fixation model.(4)The internal fixation stress of the three internal fixation models was mainly distributed in the area near the fracture end of the internal fixation device under the single-leg standing and sideways fall loads,and the peak value of internal fixation stress was the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(5)These results suggest that the mechanical stability of the femoral neck system is the best,but there may be a risk of stress shielding of the fracture end and calcar femorale.The stress of the internal fixation device of the femoral neck system is more dispersed,and the risk of internal fixation break is lower.

Objective: To investigate the distribution characteristics and related factors of weight loss behavior among middle school students in Shanghai, so as to provide a reference for guiding scientific weight loss among middle school students. Methods: From May to June 2021, a stratified cluster random sampling method was used to select 16 758 junior and high school students in 16 districts of Shanghai. Youth Risk Behavior Surveillance System was administered to assess the basic condition and weight loss behaviors of the students. An unordered multinomial Logistic regression model was employed to analyze the factors associated with weight loss behaviors. Results: A total of 5 881 (35.09%) reported engaging in exercise for weight loss, 6 344 (37.86%) reported dieting for weight loss, and 461 (2.75%) engaged in unhealthy weight loss behaviors. The unordered multinomial Logistic regression analysis indicated that compared with the no weight loss behavior group, students from urban areas( OR =1.35,95% CI =1.10-1.66), those with Internet addiction ( OR =1.71,95% CI =1.23-2.38), those with victims of bullying ( OR =2.09, 95% CI =1.68-2.61), those experiencing insomnia ( OR =2.33,95% CI = 1.74-3.11), those feelings of sadness or despair ( OR =3.10, 95% CI =2.42- 3.97 ), and those who perceived their body weight as slightly heavy ( OR =2.77, 95% CI = 2.17-3.55) or very heavy ( OR =3.41, 95% CI =2.44-4.75) were more likely to engage in unhealthy weight loss behaviors ( P <0.05). Conclusions There are significant differences in weight loss behaviors among middle school students with varying characteristics in Shanghai. Negative emotions such as insomnia and feelings of sadness or despair, Internet addiction, cognitive bias in weight and experiences of bullying are identified as related factors for unhealthy weight loss behaviors. Targeted intervention measures should be implemented to guide students towards scientific approaches to weight management.

Currently, the academic community, industry, and governmental institutions worldwide are dedicated to developing and applying artificial intelligence and other advanced analytical tools to drive the transformation of healthcare services. However, there are still many challenges, with only a few artificial intelligence tools having achieved sufficient effectiveness in improving clinical outcomes for cardiovascular diseases and strokes to be widely used. In response, the American Heart Association has formulated related scientific statements outlining the latest research developments in artificial intelligence algorithms and data science for the diagnosis, classification, and treatment of cardiovascular diseases. These statements also summarize the current best practices, research gaps, and existing challenges of artificial intelligence tools, aiming to promote the development of this field. This article interprets this scientific statement in conjunction with the relevant research practices of the author's team.

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male