BACKGROUND:Arthroscopic anchor repair has become the main treatment method for rotator cuff tears at present.Among them,the insertion status of the anchor is a key factor in the success or failure of the operation.However,currently,the impact of the insertion depth of the anchor on the stress of the bone tunnel and the anchor under different bone density conditions remains unclear.OBJECTIVE:To explore the stress distribution of the bone tunnel and the anchor when the insertion depth of the anchor varies under different bone density conditions by using three-dimensional finite element analysis technology.METHODS:The CT image data of the humerus of volunteers were collected,and the models of the humerus and the anchor were constructed by using Mimics,3-Matic,and Solidworks software.In 3-Matic,holes with distances of 0,2,4,6,and 8 mm from the surface of the humerus were respectively created at the same position of the humerus and assembled with the anchor.In Mimics,values were assigned based on the CT gray value to obtain a model with normal bone mass(T value ≥-1.0).The parameters were changed to construct models with reduced bone mass(-2.5＜T value＜-1.0)and osteoporosis(T value＜-2.5).In each model,a 70 N pulling force was applied to the anchor along the direction tangent to the inner edge of the bone tunnel.The stress distribution and magnitude of the bone tunnel and the anchor when inserted at different depths under different bone density conditions were observed.RESULTS AND CONCLUSION:(1)When the insertion depth was the same,as the bone density decreased,the maximum equivalent stress of the anchor increased,while the maximum equivalent stress of the bone tunnel decreased.(2)When the bone density was the same,as the insertion depth of the anchor increased,the maximum equivalent stress of the anchor decreased.When the insertion depth was 4 mm,the stress of the bone tunnel was the smallest and the distribution was relatively uniform.The stress of the anchor was mainly distributed around the lower anchor hole and the proximal thread,and the stress of the bone tunnel was mainly at the part in contact with the proximal thread.The increase in the insertion depth would change the uniformity and pattern of the stress distribution,while the bone density had a relatively small impact on the stress distribution pattern.(3)It is concluded that the bone density of the humerus is crucial for the anchor repair of rotator cuff tears.It is recommended that clinicians measure the bone density of the greater tuberosity of the humerus before the operation.Excessive insertion depth of the anchor does not significantly increase its stability.Clinicians can conduct personalized preoperative assessments by using the finite element analysis method in combination with the actual situation of patients to achieve the best surgical results.

BACKGROUND:Due to the complex movement of the scapula,which is a six-degree-of-freedom activity in three-dimensional space,it is difficult to measure it accurately using traditional methods.The image and model matching technology based on dual-plane X-ray is a three-dimensional measurement method that has gradually developed and matured in recent years.Two high-speed cameras are used to project and shoot from orthogonal directions.Compared with a single perspective,this method has advantages in observation range and reduction of out-of-plane errors,and is suitable for the study of scapula kinematics.OBJECTIVE:X-ray biplane and image-model registration technology were used to explore the differences in scapular kinematics between normal individuals and patients with rotator cuff tears,providing a basis for the treatment and rehabilitation of rotator cuff tear patients.METHODS:From April 2023 to January 2024,10 patients with normal shoulders and 10 patients with rotator cuff tears who met the inclusion criteria were enrolled from Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.The subjects underwent a shoulder CT scan to create a 3D model and a local scapular coordinate system.X-ray biplane images were taken during shoulder abduction with two C-arm machines.The 2D and 3D images were registered to compare scapular kinematic differences between the two groups at 0°,15°,30°,45°,60°,75°,and 90° of abduction,including scapular rotation angle and displacement distance.RESULTS AND CONCLUSION:(1)During shoulder abduction,the scapula of both groups showed upward rotation,but the upward rotation of the rotator cuff tear group was greater than that of the control group,and the difference was significant when the abduction was 30°-90°(P＜0.01).At the same time,the scapula internal rotation of both groups gradually increased,but the rotator cuff tear group was significantly greater than the control group when the abduction was 45°-90°(P＜0.01).In addition,the anteroposterior tilt of the scapula of the two groups was significantly different when the abduction was 15°-90°(P＜0.01).The scapula posterior tilt of the control group gradually increased during abduction,while the scapula of the rotator cuff tear group tilted forward except for a slight posterior tilt at 15°-30° abduction.(2)In terms of displacement,the upward displacement of the rotator cuff tear group was less than that of the control group during abduction,and the difference was significant at 15°-90°(P＜0.05),but there was no significant difference in lateral and anterior-posterior displacement between the two groups(P＞0.05).(3)Rotator cuff tear can cause scapular dyskinesis,characterized by increased upward rotation,internal rotation,and abnormal forward tilt during shoulder abduction.Identifying and addressing scapular dyskinesis is crucial for treating rotator cuff tear.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.

This paper systematically elaborates on the key points of diagnosis and differential diagnosis of salivary gland tumors characterized by a substantial amount of extracellular mucus as a main or prominent feature, and clarifies the core differential features. The term "mucus-rich" specifically denotes that mucus is a major component of the tumor, rather than a focal or minor one. This phenomenon is associated with distinct histogenetic mechanisms: it may result from specific genetic mutations (e.g., AKT1 E17K in mucinous adenocarcinoma) that drive ductal epithelial differentiation into mucus-secreting cells, or from myoepithelial cells secreting glycosaminoglycans that form a myxoid stroma. Salivary gland tumors with abundant extracellular mucus include mucinous cystadenoma, sialadenoma papilliferum-like intraductal papillary tumors, mucinous myoepithelioma, pleomorphic adenoma with mucin-rich stroma, mucinous adenocarcinoma, low-grade mucoepidermoid carcinoma, mucin-rich salivary duct carcinoma and intestinal-type adenocarcinoma. The diagnosis of these tumors is complicated by the dual nature of extracellular mucus: while it is a defining feature of some entities, it can also obscure key diagnostic architectural features in others, leading to histological overlap and inconspicuous diagnostic areas. Given the frequent histological morphological overlap among these tumors, immunohistochemical findings and molecular characteristics have emerged as crucial differential diagnostic criteria. Core differential diagnostic points include the following: histologically, there must be meticulous identification of typical structures obscured by mucin (such as squamoid cells in mucoepidermoid carcinoma and apocrine features in salivary duct carcinoma); in immunohistochemical staining, CK20 is useful for distinguishing intestinal-type adenocarcinoma (positive) from mucinous adenocarcinoma (negative), while androgen receptor aids in differentiating salivary duct carcinoma (positive) from mucoepidermoid carcinoma (negative); and molecular testing plays a critical role in definitive diagnosis (e.g., the AKT1 E17K mutation for mucinous adenocarcinoma, MAML2 rearrangement for mucoepidermoid carcinoma, and MEF2C::SS18 fusion for microsecretory adenocarcinoma). This paper systematically summarizes the core pathological features and differential diagnostic points of mucin-rich salivary gland tumors, aiming to provide a practical reference for clinical pathological diagnosis.

Objective: To understand the current status of generative artificial intelligence (GenAI) dependence among college students, explore its relationship with basic psychological needs satisfaction, artificial intelligence (AI) literacy, and academic burnout, so as to provide an empirical basis for universities to formulate scientific AI education governance strategies. Methods: A stratified cluster sampling method was used to select 1 086 undergraduates from 5 universities in Shenyang from March to May 2025. Surveys were conducted using the GenAI Dependence Scale, Basic Psychological Needs Satisfaction Scale, Academic Burnout Scale, and AI Literacy Scale. Pearson correlation analysis was utilized to examine the correlations among variables, and the PROCESS Model was employed to test the mediating effects. Results: The scores for college students GenAI dependence and academic burnout were (2.94±0.86) and (3.15±0.84), respectively. Pearson correlation analysis indicated that GenAI dependence was positively correlated with academic burnout ( r =0.40) and negatively correlated with both basic psychological need satisfaction and AI literacy ( r =-0.39,-0.22)(all P <0.01). Mediation analysis revealed that basic psychological need satisfaction partially mediated the relationship between GenAI dependence and academic burnout, with an effect size of 0.109 (95% CI =0.075-0.148). AI literacy moderated this mediation pathway, yielding a moderated mediation index of -0.051 (95% CI =-0.089 to -0.021). Under low AI literacy levels, the indirect effect of GenAI dependence on academic burnout through basic psychological needs satisfaction was stronger (effect size=0.159, 95% CI =0.108-0.201), whereas high AI literacy effectively buffered this indirect negative impact (effect size=0.058, 95% CI =0.012-0.095). Conclusion GenAI dependence not only directly predicts academic burnout but also exerts an indirect effect by thwarting psychological needs satisfaction, with AI literacy playing a moderating role.

Objective: To explore the association between warning signs of psychological and behavioral development problems with emotional and behavioral problems in preschool children in Bao an District, Shenzhen, so as to provide an empirical basis for optimizing psychological screening strategies in kindergartens. Methods: From September 2023 to August 2024, a total of 49 804 preschool children aged 4-6 years from all 401 kindergartens in Bao an District were enrolled as study subjects. The Warning Signs Checklist for Screening Psychological, Behavioral and Developmental Problems of Children and the parent version of the Strengths and Difficulties Questionnaire (SDQ) were used to assess children s developmental status and emotional and behavioral problems, respectively. Multivariable Logistic regression analysis was used to examine the association between warning signs and emotional and behavioral problems, stratified by sex. Results: The overall positive screening rate for developmental warning signs was 1.5%, and the detection rate for high risk in the SDQ total difficulties score was 6.3%. Multivariable Logistic regression analysis revealed that after adjusting for age, children who screened positive for warning signs exhibited a significantly higher risk of elevated SDQ total difficulties and subscale scores compared to those who screened negative, across both sexes (a OR boys =1.66-13.42, a OR girls =2.04-22.15, all P <0.01). The only exceptions were gross motor skills and conduct problems in boys, and personal social skills and conduct problems in girls. Notably, abnormalities in the personal social domain demonstrated the strongest association with emotional/behavioral problems (a OR boys =7.72-13.42, a OR girls =3.88-22.15), followed by the language domain (a OR boys =4.63-9.23, a OR girls =3.78-14.41) (all P <0.01). Conclusion A positive screening result for warning signs, particularly in the personal social and language domains, serves as a strong indicator of emotional and behavioral problems in preschool children.

This study establishes an integrated research paradigm based on traditional Chinese medicine （TCM） theory， guided by pathological characteristics， centered on formula compatibility principles， and supported by multidisciplinary technologies， to systematically analyze the mechanisms of hepatotoxicity induced by toxic Chinese herbal medicinals and strategies for reducing toxicity through compatibility. The findings revealed that the pathomechanism aligns closely with the "liver aversion to acute irritation" theory from Inner Canon of Yellow Emperor （《黄帝内经》）. The core pathology involves an imbalance between liver's form and function， which is characterized by malnourishment of liver form due to yin-blood depletion and dysfunction in ascending-dispersing and free-flowing activities， and closely linked to modern pathological mechanisms such as microcirculatory disturbances， oxidative stress， inflammatory response， metabolic disorder and gut-liver axis dysregulation. Based on this， a multi-layered compatibility strategy for toxicity reduction is put forward， which involves using sweet medicinals to alleviate urgency， balancing the liver form and its function， and pre-regulating other organs. This provides a theoretical basis for the safe application of toxic Chinese herbal medicinals.

By reviewing the physical biological research on the activation of acupoint effects by acupuncture， this paper explains the activation mechanism from the perspective of the generation and transmission of mechanical signals caused by acupuncture， and reveals the physical-chemical coupling processes in the acupoint microenvironment. Future research should focus on locally mechanosensitive cells， further exploring how acupuncture mechanical signals trigger dynamic changes in cells and molecules in the acupoints， and the physical-chemical information transduction mechanism， which will provide scientific evidence for the acupoint activation during acupuncture. Related studies will contribute to a deeper understanding of the scientific principles behind acupuncture and promote its clinical application and development.

Childhood adversity is a risk factor for developing depressive symptoms or suffering from depressive disorders across the life course. However， existing evidence has focused on the analysis of the association between childhood adversity and depression from a single theoretical perspective， and there is a lack of comprehensive understanding of its multiple action pathways. By systematically summarizing the cumulative risk model， sensitive-period model， recency model， risk chain model， and the stress sensitization， stress amplification， and stress inoculation hypotheses， as well as the emerging pubertal stress recalibration hypothesis， this article attempts to construct an integrated theoretical framework to elucidate the internal logical synergy among these models. Furthermore， by reviewing relevant empirical evidence， this article analyzes the applicability and limitations of different theoretical models in illustrating the underlying psychopathological mechanisms of depression. The current evidence suggests that the impact of childhood adversity on the risk of psychopathology may be driven by a complex dynamic process involving the interaction of exposure timing， cumulative load， response boundaries of biological systems， and modification by later environments. Future research should utilize longitudinal cohorts and multimodal data within a unified analytical framework to comprehensively examine the multiple pathways through which childhood adversity affects the risk of depression. This will provide a reference for precisely identifying high-risk populations， targeting "recalibration windows" represented by adolescence， developing focused intervention strategies， and ultimately blocking the cascading effects of early life adversity as early as possible. ［Funded by the "Pioneer" and "Leading Goose" R&D program of Zhejiang Province （number， 2024C03006）］