Objective To analyze the prevalence characteristics and influencing factors of metabolic syndrome (MS) in people aged 35-75 years in Hubei Province. Methods The follow-up data from 2016 to 2022 in the early screening and comprehensive intervention project for high-risk cardiovascular population in Hubei Province were collected. SAS 9.4 software was used to conduct 2-test and multivariate logistic regression to analyze the prevalence of MS and its influencing factors. Results Among the 89 199 subjects, 24 757 were affected by MS, with a prevalence rate of 27.75% and a standardized rate of 23.55%. Among the various components of MS, the prevalence of abnormal blood pressure was the highest, at 70.88%, and the standardized rate was 59.32%. Secondly, abnormal blood glucose was 36.26%, and the standardized rate was 30.04%. Central obesity was 33.12%, and the standardized rate was 30.28%. Hypertriglyceridemia was 32.90%, and the standardized prevalence rate was 32.70%. The rate of low HDL-C syndrome was 10.25%, and the standardized rate was 11.67%. The results of multivariate logistic regression analysis showed that the risk of MS increased with age, and the risk of MS in urban residents was lower than that in rural residents (OR=0.835, 95%CI: 0.77-0.886). Administrative and professional workers had a higher risk of MS than farmers (OR=1.313, 95%CI:1.194-1.445). Overweight, obesity, central obesity, history of self-reported hypertension, history of self-reported diabetes, and history of self-reported dyslipidemia were associated with a higher risk of MS, and the differences were statistically significant (P < 0.001). Conclusion The prevalence of MS is high in people aged 35-75 years in Hubei Province. On the basis of comprehensive intervention, focus monitoring should be strengthened to control the risk factors of MS and reduce the risk of cardiovascular and cerebrovascular diseases.

BACKGROUND: Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters. METHODS: A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively. CONCLUSIONS The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
Humans ; Nomograms ; Female ; Male ; Middle Aged ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/pathology* ; Aged ; Retrospective Studies ; Adenocarcinoma of Lung/pathology* ; Neoplasm Grading ; Adult

In recent years, significant progress has been made in pharmacological research on the treatment of ischemic stroke with monomeric components of traditional Chinese medicine, among which flavonoids have shown good neuroprotective effects. This article reviews the regulatory role of flavonoids in the signaling pathway of ischemic brain injury.

Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male

Background The association between occupational physical activity (OPA) and cardiometabolic risk factors remains controversial, potentially due to differences in the associations between OPA and various cardiometabolic indicators, as well as the lack of a clearly defined optimal OPA range for multiple-indicator synergistic benefits. Objective To investigate the relationship between OPA and cardiometabolic risk factors in individuals at high risk of cardiovascular disease (CVD) in Hubei Province, and to explore an optimal OPA range for multi-indicator improvements. Methods Data were derived from the Hubei Province dataset of the China Health Evaluation And Risk Reduction Through Nationwide Teamwork from 2015 to 2023, including 19028 high-risk CVD individuals. OPA (metabolic equivalent·h·d⁻¹, MET·h·d⁻¹) was assessed using the China Kadoorie Biobank physical activity questionnaire, anthropometric measurements (height, weight, waist circumference) and cardiometabolic indicators (blood pressure, fasting blood glucose, lipid profiles) were measured. Multivariate logistic regression was used to analyze the association between logOPA quartiles (Q1-Q4) and abnormal cardiometabolic indicators, while restricted cubic spline (RCS) models were employed to explore their dose-response relationships. Subgroup and interaction analyses were also conducted by gender. Results The median OPA of all participants was 12.86 MET·h·d⁻¹ (male: 14.40 MET·h·d⁻¹, female: 11.14 MET·h·d⁻¹). After adjusting for confounders, compared with the Q1 group, logOPA in the Q4 group was associated with 20% (OR=0.80, 95%CI: 0.72, 0.89), 14% (OR=0.86, 95%CI: 0.78, 0.96), and 13% (OR=0.87, 95%CI: 0.79, 0.95) reduction in the risk of abdominal obesity, low high-density lipoprotein cholesterol (HDL-C), and metabolic syndrome (MS), respectively, and 33% (OR=1.33, 95%CI: 1.18, 1.49), 33% (OR=1.33, 95%CI: 1.18, 1.50), and 38% (OR=1.38, 95%CI: 1.21, 1.57) increase in the risk of high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), and high non-HDL-C, respectively. Similar trends were observed in males and females, with significant positive associations of logOPA with high TC and high non-HDL-C in males (P_interaction<0.05). The risk of low HDL-C decreased rapidly when logOPA was more than 2.213 (OPA>9.14 MET·h·d⁻¹), showing an inverted L-shaped trend. The risk of high TC, high LDL-C, and high non-HDL-C increased beyond logOPA thresholds of 3.244, 2.476, and 3.377 (OPA >25.64, 11.89, and 29.28 MET·h·d⁻¹), respectively, showing a J-shaped trend. However, logOPA exhibited a U-shaped nonlinear dose-response relationship with blood pressure and fasting blood glucose abnormalities (P_non-linear<0.05) , with minimal risks at logOPA 2.969 and 2.372 (OPA of 19.47 and 10.72 MET·h·d⁻¹). Conclusion OPA exhibits heterogeneous dose-response patterns across cardiometabolic indicators. Integrating the inverted L-, J-, and U-shaped association patterns, we suggest an optimal OPA range of 9.14−25.64 MET·h·d⁻¹ for multiple cardiometabolic indicators in total individuals at high-risk of CVD, with ranges of 9.61−31.34 MET·h·d⁻¹ for males and 8.97−22.87 MET·h·d⁻¹ for females. These findings provide critical evidence for developing OPA interventions strategies targeting high-risk population for CVD.

Objective:To investigate the additional prognostic value of coronary CT angiography (CCTA)-based flow reserve fraction (CT-FFR) over semi-quantitative CCTA risk scores in predicting the occurrence of major adverse cardiovascular events (MACE) in type 2 diabetic patients.Methods:A total of 231 patients with type 2 diabetes mellitus who underwent CCTA at Lanzhou University from May 2020 to April 2021 were retrospectively enrolled. Clinical baseline data were collected, and patients were divided into a MACE-positive group (20 cases) and a MACE-negative group (211 cases) based on follow-up results. The CCTA images of all patients were analyzed by semi-quantitative CCTA risk score, which included coronary artery disease reporting and data system classification, segment involvement score, segmental stenosis score, Leaman score, and Leiden score. CT-FFR measurements of CCTA data of all patients were performed using Coronary Analysis software. t-test, U-test, and χ2 test were used to compare baseline parameters between MACE-positive and MACE-negative groups. The Cox proportional hazards regression model was used to analyze the relationship between semi-quantitative CCTA risk score and CT-FFR with the occurrence of MACE, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to calculate the efficacy of the prediction model established by the semi-quantitative CCTA risk score combined with CT-FFR. Results:There was no statistically significant difference in baseline data between patients in the MACE-positive and MACE-negative groups ( P>0.05), and there were significant differences in semi-quantitative CCTA risk scores and CT-FFR ( P<0.05). Multivariate Cox proportional risk regression analysis of CT-FFR≤0.80 ( HR=3.860, 95% CI 1.477-10.087, P=0.006) and Leaman score≥5 ( HR=5.210, 95% CI 1.136-23.908, P=0.029) were the best and independent predictors for the occurrence of MACE events. The combined CT-FFR and Leaman score prediction model (AUC=0.791, 95% CI 0.733-0.842, P<0.001) was a better predictor of MACE than CT-FFR alone (AUC=0.718, 95% CI 0.656-0.775, P<0.001) and Leaman score alone (AUC=0.711, 95% CI 0.648-0.768, P<0.001) both had better predictive efficacy ( Z=2.62, 1.98, P=0.009, 0.047). Conclusion:CT-FFR independently predict the occurrence of MACE in patients with type 2 diabetes mellitus and significantly improve the predictive capacity of semi-quantitative CCTA risk score for MACE.

Reporting guidelines are structured checklists for researchers to follow when reporting spe-cific types of studies.As researches conducted in real-world settings to address practical issues,implementa-tion research has stringent requirements for the replicability of result and the transparency of reporting,making its reporting guidelines particularly important.This paper systematically introduces the reporting guidelines in the field of implementation science,outlines their classification systems and scopes of applica-tion,and focuses on explaining the core characteristics and functions of five key reporting guidelines,inclu-ding the Standards for Reporting Implementation Studies(StaRI),Reporting guidelines for implementation and operational research,the Template for Intervention Description and Replication(TIDieR),the Frame-work for Reporting Adaptations and Modifications-Enhanced(FRAME),and recommendations for specifying and reporting implementation strategies.Furthermore,combined with the PEDALs research paradigm in im-plementation science,this paper further clarifies the specific application pathways for reporting guidelines and discusses directions for refinement,aiming to provide references for researchers to select appropriate reporting guidelines.

Reporting guidelines are structured checklists for researchers to follow when reporting spe-cific types of studies.As researches conducted in real-world settings to address practical issues,implementa-tion research has stringent requirements for the replicability of result and the transparency of reporting,making its reporting guidelines particularly important.This paper systematically introduces the reporting guidelines in the field of implementation science,outlines their classification systems and scopes of applica-tion,and focuses on explaining the core characteristics and functions of five key reporting guidelines,inclu-ding the Standards for Reporting Implementation Studies(StaRI),Reporting guidelines for implementation and operational research,the Template for Intervention Description and Replication(TIDieR),the Frame-work for Reporting Adaptations and Modifications-Enhanced(FRAME),and recommendations for specifying and reporting implementation strategies.Furthermore,combined with the PEDALs research paradigm in im-plementation science,this paper further clarifies the specific application pathways for reporting guidelines and discusses directions for refinement,aiming to provide references for researchers to select appropriate reporting guidelines.

Adolescent idiopathic scoliosis(AIS)is a complex three-dimensional deformity involving coronal,sagittal,and axial planes,with a prevalence that should not be overlooked.With advancements in technology and in-depth research,an increasing number of hospitals and physicians are exploring standardized diagnostic and treatment approaches for AIS.Comprehensive and in-depth understanding is required for AIS,including its etiology,screening and diagnosis,classification,assessment and examination,treatment options,exploration of current focus,and evaluation of quality of life.Such understanding ensures that the diagnostic and treatment are scientific,standardized,and timely.Based on the principles of evidence-based medicine,a consensus on the diagnosis and treatment of AIS is reached after multiple discussions among spinal surgery experts,aiming to provide reference and guidance for clinical practice.

Objective:To explore the application value of the combination of failure mode and effects analysis (FMEA) and work breakdown structure (WBS) on management of telemetry monitoring system. Methods:Seven sets of used telemetry monitoring systems in Huzhou Third People's Hospital from March 2022 to April 2024 were selected. The routine mode was used to manage the telemetry monitoring systems from March 2022 to February 2023,and the management mode of telemetry monitoring systems based on FMEA and WBS was adopted to manage the telemetry monitoring systems from May 2023 to April 2024. FMEA method was used to analyze the quantitative risk scores of various failures in 1740 uses of the telemetry monitoring system from March 2022 to February 2023,and the fault type was represented by Fx. According to the risk quantification score,the top 5 failure types included poor radio frequency (RF) signal (F2),larger pseudo-interference (F3) of electrocardiogram (ECG),abnormal measurement of blood oxygen saturation (SpO2) (F6),excessive movement and mistake touch (F8) of patients,and identification (ID) number error (F9) of telemetry emission box. WBS quality management method combined with tree chart,countermeasure fishbone chart,task assignment matrix and other tools were performed to conduct the setting of expected objectives,countermeasure analysis,task formulation and work implementation of management of telemetry monitoring system. The above indicators were compared with the changes of the top 5 failure risk grading and incidence of failure in 1850 uses of management of telemetry monitoring system from May 2023 to April 2024. Results:After the telemetry monitoring system was used to conduct management,the risk grading of the F2,F3,F6,F8 and F9 failures with highest risk grading reduced respectively to low grade,middle grade,middle grade,low grade and middle grade. At the same time,the number of F2,F3,F6,F8,F9 failures with the highest risk grading and total failures were respectively 91,89,39,45,39 and 478 times,and the incidence rates of them were 4.92％,4.81％,2.11％,2.43％,2.11％ and 25.84％,which were significantly lower than those of adopting conventional management methods. The differences of them between two methods were statistically significant (x2=2.125,6.911,9.099,2.261,2.431,32.491,P<0.05). Conclusion:The management of combined quality management method of FMEA and WBS for telemetry monitoring system can reduce risk grading and incidence rate of equipment failure,and ensure the safety of equipment in clinical use.