Objective: To analyze the temporal variation patterns of sickness absenteeism among primary and secondary school students in Shanghai, so as to explore models suitable for predicting peaks and intensity of absenteeism rates. Methods: The seasonal and trend decomposition using loess (STL) method was used to analyze the seasonal and long term trend changes in sickness absenteeism among primary and secondary school students from September 1 in 2010 to June 30 in 2018, in Shanghai. A hierarchical clustering method based on Dynamic Time Warping (DTW) was employed to classify absenteeism symptoms with similar temporal patterns. Based on historical data, the study constructed and evaluated different time series algorithms and machine learning models to optimize the accuracy of predicting the trend of sickness absenteeism. Results: During the research period, the average new absenteeism rate due to illness was 16.86 per 10 000 person day for every academic year, and the trend of sickness absenteeism exhibited both seasonality and a long term upward trend, reaching its highest point in the 2017 academic year (22.47 per 10 000 person day). The symptoms of absenteeism were divided into three categories: high incidence in winter and spring (respiratory symptoms, fever and general discomfort, etc.), high incidence in summer (eye symptoms, nosebleeds, etc.) and those without obvious seasonality (skin symptoms, accidental injuries, etc.).The constructed time series models effectively predicted the trend of absenteeism due to illness, although the accuracy of predicting peak intensity was relatively low. Among them, the multi layer perceptron (MLP) model performed the best, with an root mean squared error (RMSE) of 8.96 and an mean absolute error (MAE) of 4.37, reducing 36.51% and 39.02% compared to the baseline model. Conclusion Time series models and machine learning algorithms could effectively predict the trend of sickness absenteeism, and corresponding prevention and control measures can be taken for absenteeism caused by different symptoms during peak periods.

Given the limitations of traditional scientific research methods in revealing the complex and dynamic evolution of disease pathomechanisms， this paper analyzes the current state and challenges of the traditional Chinese medicine （TCM） pathomechanism syndrome differentiation system within the framework of systems thinking. The challenges include insufficient experimental models， low data standardization， complex nonlinear characteristics， and difficulties in integrating expert experience. By leveraging qualitative-quantitative comprehensive integration methods， this paper proposes specific research strategies， including constructing qualitative models of pathomechanism evolution， employing mathematical models for validation and quantitative analysis to reveal pathomechanism patterns， and incorporating a "human-centered" approach to achieve human-machine collaboration. These strategies aim to provide insights for the modernization and development of a new TCM pathomechanism syndrome differentiation system.

AIM: To summarize 10 surgical pearls for managing proliferative diabetic retinopathy(PDR)adapted from the ancient Chinese allusions and analyze the application of these pearls in a real-world fashion.METHODS: Retrospective, noncomparative, interventional study. Ten surgical pearls were summarized and adapted from the ancient Chinese philosophy. Totally 346 cases(443 eyes)that underwent pars plana vitrectomy(PPV)at our hospial from January 2016 to February 2024 were selected. Flexible combinations of these pearls were applied according to the specific condition of each patient during surgeries. The efficacy and safety were analyzed, as well as the application frequencies according to the existence of tractional retinal detachment or not.RESULTS: A total of 473 times of surgeries were performed on all the patients. According to ancient Chinese allusions, ten surgical pearls were summarized from these surgeries. All PPVs went smoothly with the application of different combinations. Finally, almost all proliferative membranes were successfully peeled except for 10 patients(11 eyes), who went through strategy No.10(minimal membranectomy)that, only necessary relaxation incisions were made with most of the proliferative membranes left on purpose. The final visual acuities were mostly improved or stable(1.92±0.83 LogMAR preoperatively vs 1.16±0.85 LogMAR postoperatively, P<0.01). Postoperative complications mainly included early inflammatory responses in the anterior chamber and nuclear sclerosis. Recurrent vitreous hemorrhage, retinal detachment, and hyphema or neovascular glaucoma occurred in 1.9%(9/473), 3.2%(15/473), 0.4%(2/473)and 0.4%(2/473)times of PPVs, respectively. After 12/473(2.5%)times of PPVs, retinal detachment at the macular area still existed, and multiple times of subsequent PPVs were conducted. Final retinal attachment at the macular area was realized in 98.9% eyes. Those 5 unattached eyes were with heavily reproliferated membranes and subsequent tractional retinal detachment recurrence under the oil, and three of them were scleral buckled additionally.CONCLUSION:These 10 surgical strategies and technique pearls were mostly effective and safe in the management of severe PDR patients. They were relatively easy to be memorized and applicated once the meaning of each Chinese idiom was understood. One can use different combinations flexibly according to a patient's specific condition.

ObjectiveTo analyze the epidemiological characteristics and trends of hand-foot-mouth disease (HFMD) in Hangzhou, so as to provide an evidence for developing effective prevention and control measures and evaluating the control effects. MethodsThe incidence data of HFMD in Hangzhou were collected from the Infectious Disease Reporting Information Management System of China Information System for Disease Control and Prevention. Descriptive epidemiology was applied to analyze the temporal, spatial and demographic distribution characteristics and etiology monitoring results of HFMD cases in Hangzhou from 2010 to 2023. Joinpoint regression model was used to analyze the trends of incidence rate of HFMD. Furthermore, circular distribution method was utilized to calculate the incidence peak of HFMD. ResultsFrom 2010 to 2023, the average annual reported incidence rate of HFMD in Hangzhou was 138.85/100 000, the proportion of severe cases was 0.04%, the mortality rate was 0.01/100 000, and the case fatality rate was 5.30/100 000. Both the total incidence rate and the incidence rate by sex showed an increasing trend. The annual reported incidence rate in males (158.72/100 000) was higher than that in females (117.61/100 000). The reported incidence rate showed a significant seasonal characteristic, with summer being the peak of epidemic. The results of surveillance samples suggested that the prevalence of HFMD in Hangzhou is characterized by the co-existence of multiple pathogens, with EV-A71 and CV-A16 being the dominant pathogens in the previous years and CV-A6 being the dominant pathogen since 2018. The proportion of EV-A71 in severe cases (77.19%) was higher than that in ordinary cases (15.37%), in addition, its proportion in ordinary cases, severe cases, and fatal cases all showed a decreasing trend. ConclusionThe incidence rate of HFMD in Hangzhou is still high, so it’s still necessary to continue to strengthen the prevention and control measures for key populations. In recent years, CV-A6 has been the main prevalent pathogen in Hangzhou. Further efforts in pathogen detection and analysis should be enhanced in the future.

Non-invasive body fluids contain a wealth of health-related biomarkers.Monitoring these biomarkers can provide valuable information for disease diagnosis,health management,drug abuse screening,and sports performance optimization.In this work,a carbon nanotube-polysiloxane(CNT-Putty)-based wearable electrochemical sensor was constructed on glove by screen-printing method.This electrode material had not only a simple composition,but also a relatively simple synthesis process.In addition,the electrode exhibited superior electrochemical performance compared to commercial screen-printed electrodes.When applied to uric acid(UA)detection in three different body fluids,the CNT-Putty working electrode demonstrated excellent linearity,selectivity,and a low detection limit.The wearable glove sensor could successfully monitor UA levels in body fluids under varying dietary conditions,indicating its potential for personalized UA monitoring and management.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

BACKGROUND: Valvular heart disease (VHD) has become increasingly common with the aging in China. This study aimed to evaluate regional differences in the clinical features, management strategies, and outcomes of patients with VHD across different regions in China. METHODS: Data were collected from the China-VHD Study. From April 2018 to June 2018, 12,347 patients who presented with moderate or severe native VHD with a median of 2 years of follow-up from 46 centers at certified tertiary hospitals across 31 provinces, autonomous regions, and municipalities in Chinese mainland were included in this study. According to the locations of the research centers, patients were divided into five regional groups: eastern, southern, western, northern, and central China. The clinical features of VHD patients were compared among the five geographical regions. The primary outcome was all-cause mortality or rehospitalization for heart failure. Kaplan-Meier survival analysis was used to compare the cumulative incidence rate. RESULTS: Among the enrolled patients (mean age, 61.96 years; 6877 [55.70%] male), multiple VHD was the most frequent type (4042, 32.74%), which was mainly found in eastern China, followed by isolated mitral regurgitation (3044, 24.65%), which was mainly found in northern China. The etiology of VHD varied significantly across different regions of China. The overall rate of valve interventions was 32.67% (4008/12,268), with the highest rate in southern China at 48.46% (205/423). In terms of procedure, the proportion of transcatheter valve intervention was relatively low compared to that of surgical treatment. Patients with VHD in western China had the highest incidence of all-cause mortality or rehospitalization for heart failure. Valve intervention significantly improved the outcome of patients with VHD in all five regions (all P  <0.05). CONCLUSIONS: This study revealed that patients with VHD in China are characterized by significant geographic disparities in clinical features, treatment, and clinical outcomes. Targeted efforts are needed to improve the management and prognosis of patients with VHD in China according to differences in geographical characteristics. REGISTRATION ClinicalTrials.gov , NCT03484806.
Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Heart Valve Diseases/therapy* ; Kaplan-Meier Estimate ; Tertiary Care Centers ; Treatment Outcome

The present study delves into the cellular mechanisms underlying the antiviral effects of dehydrodiisoeugenol(DEH) by focusing on the transcription factor EB(TFEB)/autophagy-lysosome pathway. The cell counting kit-8(CCK-8) was utilized to assess the impact of DEH on the viability of human non-small cell lung cancer cells(A549). The inhibitory effect of DEH on the replication of influenza A virus(H1N1) was determined by real-time quantitative polymerase chain reaction(RT-qPCR). Western blot was employed to evaluate the influence of DEH on the expression level of the H1N1 virus nucleoprotein(NP). The effect of DEH on the fluorescence intensity of NP was examined by the immunofluorescence assay. A mouse model of H1N1 virus infection was established via nasal inhalation to evaluate the therapeutic efficacy of 30 mg·kg~(-1) DEH on H1N1 virus infection. RNA sequencing(RNA-seq) was performed for the transcriptional profiling of mouse embryonic fibroblasts(MEFs) in response to DEH. The fluorescent protein-tagged microtubule-associated protein 1 light chain 3(LC3) was used to assess the autophagy induced by DEH. Western blot was employed to determine the effect of DEH on the autophagy flux of LC3Ⅱ/LC3Ⅰ under viral infection conditions. Lastly, the role of TFEB expression in the inhibition of DEH against H1N1 infection was evaluated in immortalized bone marrow-derived macrophage(iBMDM), both wild-type and TFEB knockout. The results revealed that the half-maximal inhibitory concentration(IC_(50)) of DEH for A549 cells was(87.17±0.247)μmol·L~(-1), and DEH inhibited H1N1 virus replication in a dose-dependent manner in vitro. Compared with the H1N1 virus-infected mouse model, the treatment with DEH significantly improved the body weights and survival time of mice. DEH induced LC3 aggregation, and the absence of TFEB expression in iBMDM markedly limited the ability of DEH to counteract H1N1 virus replication. In conclusion, DEH exerts its inhibitory activity against H1N1 infection by activating the TFEB/autophagy-lysosome pathway.
Influenza A Virus, H1N1 Subtype/genetics* ; Animals ; Autophagy/drug effects* ; Humans ; Mice ; Basic Helix-Loop-Helix Leucine Zipper Transcription Factors/genetics* ; Influenza, Human/metabolism* ; Lysosomes/metabolism* ; Orthomyxoviridae Infections/genetics* ; Eugenol/pharmacology* ; Antiviral Agents/pharmacology* ; Virus Replication/drug effects* ; A549 Cells ; Male