Background Fatigue among distribution network live-line workers in complex operational environments has become increasingly severe and requires widespread attention. Objective To investigate the positive rates of fatigue and associated influencing factors of live-line power distribution workers, and to make a reasonable strategy to reduce the fatigue of front-line workers. Methods Power supply companies in Guangdong, Guangxi, and Yunnan provinces were selected by cluster sampling in 2023, and all front-line live-line workers in the selected companies were recruited. The questionnaire used in this study consisted of two parts: one was the Fatigue Scale-14 (FS-14) for investigating fatigue status and the other was for associated influencing factors. A FS-14 score greater than 3 points was defined as fatigue. \begin{document}$ {\chi }^{2} $\end{document} test and logistic regression were employed to analyze the factors affecting fatigue. Results A total of 1341 questionnaires were distributed, and 1217 valid questionnaires were recovered, with a valid response rate of 90.75%. The average fatigue score of the participants was (6.84±3.79) points, and a total of 1054 participants scored over 3, resulting a fatigue positive rate of 86.6%. The logistic regression model showed that working under extreme weather (OR=2.03, 95%CI: 1.38, 2.98), overtime 2-3 times a week (OR=5.24, 95%CI: 1.28, 21.40), overtime everyday (OR=6.95, 95%CI: 1.71, 28.27), frequent wrist rotation (OR=1.95, 95%CI: 1.08, 3.51), and leaning back slightly (OR=2.94, 95%CI: 1.19, 7.28) associated with higher positive rates of fatigue. Conclusion The high positive rate of fatigue among live-line power distribution workers suggests severe fatigue issues. Extreme weather conditions, working overtime frequency, and maintaining wrist rotation and backward bending postures during work are main factors affecting their fatigue.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

Acute pancreatitis （AP） often has an acute onset， and although most cases are self-limiting， there is still a risk of rapid deterioration， posing a serious threat to the lives of patients. Currently， traditional assessment tools such as clinical scoring systems and serum biomarkers are widely used to determine disease severity and prognosis. To enhance the accuracy of assessment， combined detection strategies integrating different scores and biomarkers are often used in clinical practice； however， there are still certain limitations such as insufficient specificity， limited early sensitivity， and a lack of consistent cut-off values. Therefore， current studies are actively exploring novel biomarkers with superior specificity and sensitivity and continuously optimizing their combined application strategies. This article summarizes the current application status of traditional assessment tools and reviews the research advances in novel biomarkers.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To report a case of tibial synovial sarcoma and review relevant literature to enhance understanding of this disease.Methods The clinical data of a patient with tibial synovial sarcoma treated at Kaifeng Central Hospital were retrospectively analyzed.A literature search was conducted in domestic and international databases,including China National Knowledge Infrastructure(CNKI),Wanfang Data,PubMed,Web of Science,and Embase,up to July 2024.Relevant literature was comprehensively reviewed to summarize the imaging and pathological characteristics,treatment,and prognosis of synovial sarcoma.Results A 29-year-old female patient was admitted with left lower extremity pain.X-ray examination revealed a proximal tibia space-occupying lesion suggestive of malignancy,and a mid-tibial space-occupying lesion considered benign.Contrast-enhanced computed tomography(CT)and plain magnetic resonance imaging(MRI)of the proximal tibial lesion also suggested malignancy.Ultrasound-guided biopsy of the proximal tibial tumor revealed a poorly differentiated malignant tumor.Immunohistochemistry results indicated monophasic synovial sarcoma,requiring genetic testing for definitive diagnosis.The patient underwent wide resection of the proximal left tibial malignancy with tumor-type artificial joint replacement,combined with curettage and bone cement filling for the left mid-tibial lesion under anesthesia.Postoperative pathology of space-occupying lesions in the proximal tibia confirmed monophasic synovial sarcoma,and fluorescence in situ hybridization(FISH)demonstrated a rupture of the synovial sarcoma translocation gene(SYT)(i.e.,SS18 positive).There was no recurrence or metastasis found in the patient during the reexamination 6 months after postoperative chemotherapy.As of July 2024,15 cases of genetically confirmed primary intraosseous synovial sarcoma have been reported internationally.Symptoms included pain and swelling,with a medical history of 1-2 years.The X-ray and CT findings showed osteolytic destruction with bone cortical discontinuity.In 13 cases,the intraosseous masses extended to the extraosseous area;in 2 cases,punctate calcifications were detected within the masses.Plain MRI scan showed iso-signal or hypo-signal on T1WI and hyper-signal,iso-signal,and hypo-signal on fat-suppressed T2WI,and enhanced MRI scan demonstrated heterogeneous enhancement.Pathological examination showed spindle-shaped cells under microscopy.Immunohistochemistry results showed positive epithelial membrane antigen(EMA),broad-spectrum cytokeratin(AE1/AE3),Ewing's sarcoma marker(CD99),and transducin-like enhancer of Split 1(TLE1).Twelve patients underwent surgical treatment;6 patients received adjuvant chemotherapy after surgery,of whom 4 developed local recurrence or distant metastasis at initial diagnosis,and 3 died during follow-up.Among the 6 patients who did not receive adjuvant chemotherapy,3 suffered from recurrence or distant metastasis.Conclusions Primary intraosseous synovial sarcoma is a rare malignant tumor with non-specific clinical manifestations.Imaging features typically include osteolytic destruction and intraosseous masses extending extraosseously,suggesting an intraosseous origin.Pathology and immunohistochemistry aid diagnosis,but definitive confirmation relies on further genetic testing.At present,the main treatment regimens for synovial sarcoma involve comprehensive therapies such as surgery and adjuvant chemotherapy,and the prognosis of patients is poor.

OBJECTIVE: To explore the genetic basis for a boy affected with Cohen syndrome. METHODS: A boy admitted to Children's Hospital of Nanjing Medical University in January 2021 was selected as the study subject. Genome DNA was extracted from peripheral blood samples from the child and his parents. Whole exome sequencing (WES) was carried out. And candidate variants were verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 202106060-1). RESULTS: WES revealed that the child has harbored compound heterozygous variants of the VPS13B gene, namely c.1563+1G>A and c.3007insC (p.A1003Afs*13), which were inherited from his mother and father, respectively. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), both variants were rates as pathogenic. The c.3007insC (p.A1003Afs*13) was unreported previously. CONCLUSION The compound heterozygous variants c.1563+1G>A and c.3007insC (p.A1003Afs*13) of the VPS13B gene probably underlay the pathogenesis of Cohen syndrome in this child. Above finding has enriched the mutational spectrum of VPS13B gene.
Humans ; Male ; Vesicular Transport Proteins/genetics* ; Intellectual Disability/genetics* ; Muscle Hypotonia/genetics* ; Microcephaly/genetics* ; Fingers/abnormalities* ; Myopia/genetics* ; Obesity/genetics* ; Developmental Disabilities/genetics* ; Mutation ; Exome Sequencing ; Child ; Heterozygote ; Retinal Degeneration

Objective To explore the correlation between visceral fat area(VFA)and triglyceride-glucose(TyG)index in people with normal body mass index(BMI)based on quantitative CT(QCT).Methods Totally 1 358 healthy adults with normal BMI who underwent QCT body composition measurement were retrospectively enrolled and divided into normal visceral fat group(normal group,VFA＜100 cm2,n=509),mild visceral fat accumulation group(mild group,100 cmn2≤VFA＜150 cm2,n=431)and severe visceral fat accumulation group(severe group,VFA≥150 cm2,n=418)according to VFA measured with QCT.Pearson and Spearman correlation analysis were performed to observe correlations of TyG index with general data and body composition in 20-40,41-60,61-90 years old men and women with normal BMI,respectively.Multiple linear regression analysis was performed to screen the independent impact factors of TyG index in normal BMI people.Results Low density lipoprotein cholesterol(LDL-C),total cholesterol(TC),glutamic-pyruvic transaminase(GPT),VFA and subcutaneous fat area(SFA)of men with normal BMI at different age stages were weakly-lowly positively correlated with TyG index(r=0.143-0.459,all P＜0.05),while high-density lipoprotein cholesterol(HDL-C)were weakly-lowly negatively correlated with TyG index(r=-0.410--0.222,all P＜0.05).LDL-C,VFA and SFA of women with normal BMI at different age stages were weakly-lowly positively correlated with TyG index(r=0.206-0.495,all P＜0.05).GPT,TC,VFA and liver fat content(LFC)were all independent risk factors,while HDL-C was independent protective factor for TyG index in people with normal BMI(all P＜0.05).Conclusion VFA was positively correlated with TyG index in people with normal BMI.Measurement of abdominal body composition based on QCT combined with lipid indexes had certain potential for predicting insulin resistance and relative metabolic diseases in abdominal obese people.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.