Purpose: Dual anti–human epidermal growth factor receptor 2 (HER2) drugs have become the standard regimen for neoadjuvant systemic treatment (NST) to HER2-positive breast cancer patients. However, the efficacy varies greatly among patients with different HER2 protein expression levels. Materials and Methods: A total of 575 HER2-positive breast cancer patients from multiple centers throughout China from 2013 to 2022 were retrospectively analyzed. We compared clinicopathological features in different HER2 immunohistochemistry classes (HER2 2+/in situ hybridization [ISH] + or HER2 3+), and their difference in response to NST and survival with single or dual anti-HER2 drugs. Drug sensitivity assays were used to evaluate different efficacy of anti-HER2 drugs in vitro. Results: Compared to HER2 3+ subgroup, the HER2 2+/ISH+ group had a higher proportion of hormone receptor–positive status (48.7% vs. 76.1%, p < 0.001), more HER2 protein loss after NST, lower pathological complete response (pCR) rate (46.07% vs. 16.24%, p < 0.001), and tended to have worse disease-free survival (DFS). In HER2 2+/ISH+ patients, treated with pertuzumab and trastuzumab in combination had no significant improvement in pCR (19.12% vs. 12.24%, p=0.287) and DFS (p=0.908) than using alone. Drug sensitivity assay showed poor efficacy with dual anti-HER2 drugs in HER2 2+/ISH+ cell lines; however, fam-trastuzumab deruxtecan drugs had a satisfactory effect. Conclusion Owing to the differences in clinicopathological features and treatment efficacy, we considered the HER2 2+/ISH+ group to be a distinct subtype and defined it as the HER2-moderate–positive subgroup. In this subgroup, dual anti-HER2 drugs did not exert significant improvement in pCR and DFS. Therefore, treatment optimization is warranted, with antibody-drug conjugate drugs as potential options.

Objective: To analyze the intervention effects of moderate to vigorous classroom physical exercise on psychological sub health among Uyghur junior high school students, providing reference for mental health promotion and intervention in the ethnic group. Methods: From April to July 2022, 647 eighth grade Uighur junior high school students from two middle schools in the Kashgar Prefecture, Xinjiang, were selected using a random cluster digital sampling method. They were randomly assigned to a control group ( n =326) and an intervention group ( n =321). While adhering to the regular curriculum, the intervention group additionally received 12 week, 36 session (every Monday, Wednesday, and Friday afternoon) moderate to vigorous classroom physical exercise intervention (45 minutes per session, including warm up, main exercises, and cool down activities). Self administered psychological sub health was completed through Brief Instrument on Psychological Health of Youths before and after intervention, and the data were analyzed using covariance analysis. Results: Before intervention, there were no statistically significant differences between the control and intervention groups in the total scores for psychological sub health or in the scores for the three dimensions including emotional problems, conduct problems, and social adaptation difficulties ( t =0.76, 0.40, 0.01, 0.94 , all P >0.05). After intervention, the scores of the intervention group s junior high school students on the emotional problems, conduct problems, and social adaptation difficulties dimensions decreased by 0.50, 0.28, and 0.18, respectively, compared with the control group, and all differences were statistically significant ( t =3.35, 3.36, 2.07, all P <0.05). The main effect of the psychological sub health category was statistically significant ( F =60.66, P <0.01, partial η 2=0.09). Compared with the control group, the psychological sub health scores of junior high school students in the intervention group decreased by 0.96 ( t =3.42, P < 0.05 ). Conclusion The 12 week moderate to vigorous classroom physical exercise intervention has a positive effect on the psychological sub health of Uyghur junior high school students.

Background Occupational disease diagnosis appraisal is an appeal procedure initiated when a party disagrees with the occupational disease diagnosis conclusion. It is a legal procedure to safeguard the health rights of employees and the legitimate rights and interests of employers. Objective To analyze the data of occupational disease diagnosis appraisal in Shanghai, identify existing problems, and provide suggestions for improving. Methods Statistical analysis was conducted on basic situation, characteristics of workers and employers, disease classification, and consistency between diagnosis and appraisal conclusions of all occupational disease diagnosis appraisal cases from 2012 to 2024. All diagnosis conclusions were named in accordance with the Classified Catalogue of Occupational Diseases included employers were classified according to the Measures for the Statistical Classification of Large, Medium, Small and Micro Enterprises" and the Notice on Adjusting the Provisions for Classifying Enterprise Registration Types; industry classification followed the Industrial Classification for National Economic Activities (GB/T 4754-2017). Results From 2012 to 2024, a total of 260 cases of occupational diseases diagnosis appraisal were closed. The main diagnosis were 138 cases of ear, nose, throat, and oral diseases (53.1%), chemical poisoning (18.8%), and pneumoconiosis and other respiratory diseases (16.2%). The proportion of ear, nose, throat, and oral diseases gradually increased (2022 excluded). The incidence of annual municipal and provincial cases decreased by 76.3% and 85.0% respectively (2024 vs 2012). The consistency rate of conclusions between appraisal and diagnosis gradually increased. The total consistency rates between final appraisals and diagnostic conclusions were 81.5%, and 79.7%, 87.8%, 100.0%, 85.0% for ear, nose, throat, and oral diseases, chemical poisoning, and pneumoconiosis and other respiratory system diseases, respectively. Specifically, the consistency rates were 80.1% and 80.0% for noise-induced deafness and chronic benzene poisoning. In terms of inconsistency reasons, "inconsistent interpretation of standards and excessive discretion in standard application" accounted for 43.8%, followed by "failure to correctly apply standards" (31.3%) and and "discrepancies in occupational exposure history recognition" (15.6%). Conclusion The revisions and improvements of the legal system for occupational disease diagnosis appraisal have played a positive role in improving the consistency of conclusions between appraisal and diagnosis of occupational diseases in Shanghai. It is suggested to further strengthening the publicity and training of occupational disease diagnosis standards to improve the quality of occupational disease diagnosis.

Critically ill patients often experience significant skeletal muscle wasting due to prolonged bed rest, metabolic disorders, inflammatory responses and malnutrition, which affects the patient's mobility and may also lead to increased mortality. Timely and accurate assessment of muscle status is important for optimizing treatment strategies and improving patient prognosis. There are various limitations in the current methods of assessing muscle mass, and muscle ultrasound, as a noninvasive, convenient, low-cost and suitable technique for bedside monitoring, has received increasing attention for its application in muscle assessment of critically ill patients. However, there are still a number of challenges in its practical application, such as the lack of uniform standards for the measurement method, the high dependence on the operation, and the reproducibility of the data that needs to be optimized, and so on. The aim of this article is to systematize the research progress of muscle ultrasound in muscle assessment of critically ill patients, and to discuss the advantages and limitations of its clinical application, in order to provide a scientific basis for future research and clinical practice.
Humans ; Critical Illness ; Ultrasonography ; Muscle, Skeletal/diagnostic imaging*

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective To evaluate the clinical outcome of neurosurgical robot-assisted stereotactic puncture and drainage for brain abscess.Methods A retrospective analysis was conducted on the clinical data of 53 patients with brain abscess admitted to our hospital from January 2018 to December 2024.Among them,29 cases underwent craniotomy for abscess resection(craniotomy group),while 24 cases received neurosurgical robot-assisted stereotactic puncture and drainage(robot-assisted group).The operation time,intraoperative blood loss,decompressive craniectomy rate,proportion of postoperative antibiotic regimen adjustment,postoperative hospital stay,incidence of postoperative complications,mortality rate and Glasgow outcome scale(GOS)scores 6 months after surgery of patients were compared between the two groups.Results Compared with the craniotomy group,the robot-assisted group demonstrated significantly shorter operation time,less intraoperative blood loss,and lower incidence of postoperative complication,the differences were all statistically significant(P<0.05).However,there were no statistically significant differences in terms of decompressive craniectomy rate,postoperative hospital stay,mortality rate,GOS score,or proportion of the postoperative antibiotic regimen adjustment between the two groups(P>0.05).Conclusion As a precise and minimally invasive surgical method,neurosurgical robot-assisted stereotactic puncture and drainage for patients with brain abscess can effectively improve the operational efficiency,shorten the operation time,reduce intraoperative injury,and lower the risk of postoperative complications.It has high clinical application value and potential for widespread adoption.

Objective:To use machine learning to predict the efficacy of accelerated corneal collagen cross-linking (A-CXL) surgery, identify prognostic factors, and construct models to predict postoperative disease progression.Methods:A single-center retrospective study was conducted.A total of 82 keratoconus patients (112 eyes) who underwent A-CXL surgery at the West China Hospital of Sichuan University between March and December 2021 were enrolled.Preoperative and follow-up examinations included anterior segment evaluation by slit-lamp microscopy, corneal topography using Pentacam, and corneal biomechanical indices using Corvis ST.Disease progression was defined as an increase in maximum keratometry (Kmax) of ≥1 D from the preoperative level at the last follow-up.Various machine learning algorithms were employed to analyze corneal topography, biomechanical parameters and corneal densitometry values to identify prognostic factors and construct models for predicting postoperative disease progression.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (No.2023496).Written informed consent was obtained from each subject.Results:During follow-up, 15.1% (17/112) of the eyes showed progression after A-CXL.The preoperative astigmatism and stress-strain index (SSI) in the progression group were (-5.41±2.72)D and 1.41±0.78, respectively, which were significantly higher than (-3.30±2.54)D and 0.95±0.98 in the non-progression group ( t=2.80, 2.03; both P<0.05).Cox regression analysis identified preoperative astigmatism (hazard ratio [HR]=1.20), SSI (HR=1.10), and anterior corneal densitometry of 2-6 mm (CDA6) (HR=2.10) as significant risk factors for post-A-CXL progression.Among various machine learning models developed and validated, the area under the curve (AUC) values for logistic regression, multilayer perceptron (MLP) model, and random forest (RF) exceeded 0.700.For F1-score, the AUC values for logistic regression, MLP, and RF were 0.870, 0.880, and 0.880, respectively.The network structure of the visualized MLP was a single-layer, 24-neurons neural network with 80% accuracy in predicting whether progression occurred after A-CXL.The clinical nomogram developed in conjunction with astigmatism, SSI, and CDA6 predicted the cumulative probability of progression at 0.5, 1, and 2 years postoperatively based on the sum of the specified values for each variable, and based on the optimal cutoff value, keratoconus corneas could be classified into high-, intermediate-, and low-risk groups, respectively.The time-dependent subject operating characteristic curves of the nomogram showed AUCs of 0.734, 0.685, and 0.935 at 0.5, 1, and 2 years postoperatively, respectively, all of which performed well in predicting progression. Conclusions:Preoperative astigmatism, SSI, and CDA6 are significant risk factors for post-A-CXL progression in keratoconus.The MLP model can accurately predict postoperative disease progression, and the clinical nomogram combining preoperative astigmatism, SSI, and CDA6 can effectively differentiate between low-, medium-, and high-risk postoperative progression outcomes.