Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective To explore the effectiveness of"near peer learning"(NPL)in the electromyo-graphy(EMG)teaching module for neurology residents.Methods The Department of Neurology,Peking Union Medical College Hospital implemented an NPL instructional design for a course on EMG for residents from November 2020 to March 2024.This teaching session was held annually,in which senior residents in-structed juniors who were 1 or 2 years earlier in their training.The residents participated in the pre-course/post-course tests and completed a feedback survey at the end of the session.This evaluation method was used to un-derstand the effectiveness of the NPL intervention in EMG teaching.Results Over four years,a total of 83 residents participated.Among them,there were 24 postdoctoral students,52 postgraduates and 7 junior resi-dents.The results showed that the post-course test scores were significantly improved compared with pre-course test scores(74.33±2.43 vs.70.11±2.49,P=0.005),with the most remarkable improvements seen for"tu-tees"(73.84±20.53 vs.70.29±21.46,P=0.020),postgraduates(74.04±22.51 vs.68.97±21.40,P=0.009),first-year residents(70.19±4.02 vs.63.59±3.59,P=0.040)and first-time participating resi-dents(65.23±3.24 vs.60.97±3.21,P=0.030).The post-program feedback showed that both tutors and tu-tees thought highly of NPL,believing that it enabled them to gain knowledge and helped them to improve teaching skills.Conclusions The NPL intervention is suitable for the teaching of EMG,because of its contri-bution to knowledge acquisition and basic clinical skills improvement.The NPL is worth replicating in other teaching and learning programs.

Objective To analyze temporal trends in infectious disease burden among Chinese elderly(≥60 years)using data from the Global Burden of Disease Study(GBD 2021),evaluate age-period-cohort effects on disease burden,predict trends through 2045,and propose evidence-based medication management strategies.Methods We conducted a threefold analysis of infectious disease burden from 1990 to 2021 using Joinpoint regression to identify temporal trends,Age-Period-Cohort(APC)modeling to disentangle epidemiological effects,and Nordpred projections for 2045 disease burden estimates.Results Over 32 years,all infectious disease categories except HIV/AIDS and sexually transmitted infections demonstrated significant declines.Enteric infections showed the most rapid reductions in mortality(AAPC=-7.85,P﹤0.001)and disability-adjusted life year rates(DALYR;AAPC=-7.18,P﹤0.001).We also found a significant decrease in the incidence of tropical diseases and malaria(AAPC=-6.77,P﹤0.001).APC analysis found that the age effect was mostly negative in terms of the annual percentage change in mortality and DALYR for each disease,except for HIV/AIDS,with an overall decline in period risk over time,and a generally higher risk of morbidity and mortality for the early birth cohort,and an overall decline in the risk of each disease as the year of birth progressed,but the risk of HIV/AIDS death and DALY period and cohort risks trended upward.While the overall risk for certain diseases was slightly higher in males compared to females,the trends were largely consistent across both sexes.In terms of projections,the absolute prevalence of respiratory infections and tuberculosis in terms of number of cases and age-standardized rates,and the growth of the disease burden of HIV/AIDS were particularly prominent,making them important health challenges for the future.The burden of disease in the elderly often results in issues such as potential polypharmacy,which must be addressed to improve medication management.Conclusions China has achieved remarkable progress in reducing infectious disease burdens among older adults,though HIV/AIDS and sexually transmitted infections present an escalating public health threat.These findings advocate for enhanced surveillance systems,age-specific prevention strategies,and precision medication protocols to optimize therapeutic outcomes in geriatric populations.

Objective:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

AIM:This study aims to investigate the protective effect of α-lipoic acid(α-LA)against alcohol-induced damage in H9c2 rat cardiomyocytes and to explore the underlying mechanisms.METHODS:An alcohol-induced injury model of H9c2 cells was established,and the cells were divided into 4 groups:control group,alcohol group,α-LA group,and alcohol+α-LA group.Additionally,H9c2 cells overexpressing aldehyde dehydrogenase 2(ALDH2)were cre-ated and further divided into 6 groups:normal control group,normal cells treated with alcohol group,normal cells treated with alcohol+α-LA group,ALDH2 overexpression group,ALDH2-overexpressing cardiomyocytes treated with alcohol group,and ALDH2-overexpressing cardiomyocytes treated with alcohol+α-LA group.Cell proliferation was assessed using 5-ethynyl-2'-deoxyuridine(EdU)staining.Reactive oxygen species(ROS)levels in each group were measured using di-hydroethidium(DHE)staining,while the expression levels of ALDH2,silent information regulator 1(SIRT1),heme oxy-genase 1(HO1)and P53 proteins were detected by Western blot analysis.RESULTS:(1)Alcohol exposure resulted in a decrease in the proliferation of H9c2 cells and an increase in intracellular oxidative stress,evidenced by elevated ROS levels and decreased expression of related proteins(ALDH2,SIRT1 and HO1).However,α-LA treatment significantly mitigated the decline in cell proliferation and the oxidative stress induced by alcohol.(2)Alcohol may induce cellular se-nescence,as demonstrated by the up-regulation of P53 expression,which were reversed by α-LA.(3)The H9c2 cells with high ALDH2 expression markedly improved the cell proliferation in the presence of alcohol,suppressed the ROS pro-duction,prevented the down-regulation of oxidative stress-related proteins(ALDH2,SIRT1 and HO1),and reversed the enhanced expression of the senescence marker P53.CONCLUSION:Treatment with α-LA may counteract oxidative stress and attenuate cellular senescence by activating ALDH2,thereby protecting cardiomyocytes from alcohol-induced damage.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

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 investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Facing the requirements of promoting the healthy China initiative and improving people's health, the "bone health program" was proposed in 2024. In-depth development of a traditional Chinese medicine(TCM) prevention and control system is of strategic significance to the implementation of the "bone health program". Focusing on osteoporosis(OP), a representative disease affecting people's bone health, this paper concludes that accelerating the research on the prevention and control of OP by TCM is conducive to enhancing the knowledge and awareness of OP among the public, and it is beneficial to revealing the evolutionary pattern of OP and improving the understanding and management of this disease. Additionally, it can provide an overall framework for and strengthen the systematicity and completeness of the research on the prevention and treatment of OP by TCM. Meanwhile, it can help to explore new research paradigms and optimize the existing research model, so as to promote innovative breakthroughs in the prevention and treatment of bone health-related diseases by TCM. Under the overall layout of the "bone health program", importance should be attached to the early prevention and the innovation of very early diagnosis and intervention of OP. Emphasis should be put on the discovery of the target network of disease and treatment mechanism for revealing the core pathogenesis of OP and the therapeutic mechanism of TCM. In addition to local lesions of the bone and its clinical outcomes, attention should be paid to the development of multiple metabolic complications. The fusion of advanced interdisciplinary technologies should be promoted for OP and its complications, and thus a research and development system based on clinical application scenarios and driven by big data can be built. The measures above will facilitate the progress in the prevention and treatment of OP and other bone diseases by TCM and provide new momentum for enriching and deepening the research connotation of the "bone health program".
Osteoporosis/therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/therapeutic use* ; China ; Bone and Bones/drug effects*

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.