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

In recent years, with the continuous development and increasing maturity of interventional techniques, interventional treatment for congenital heart disease (CHD) has been progressively disseminated to county- and city-level hospitals in China. Concurrently, the standardized management of adult CHD (particularly patent foramen ovale) and the lifelong management of complex CHD are gaining increasing clinical attention, while the emergence of new techniques and products continuously advances the discipline. This article aims to review the new progress made in the field of interventional treatment for congenital heart disease in China during 2024. It specifically reviews and analyzes the following key aspects: (1) annual statistics on interventional closure procedures for CHD; (2) recent insights into patent foramen ovale closure; (3) advances in transcatheter pulmonary valve replacement; (4) interventional treatment and lifelong management strategies for complex CHD; (5) new interventional techniques for acquired heart disease; and (6) the application of artificial intelligence in CHD management. Through the synthesis and discussion of these topics, this article seeks to provide a detailed analysis of the current landscape of interventional treatment for CHD in China and project its future development trends.

This paper presents an automatic acquisition and analytic procedure of acupuncture manipulation based on optical navigation, aiming at solving the shortcomings of existing acquisition methods of acupuncture manipulation. An acquisition holder installed at the handle tail of filiform needle was designed to display the movement trajectory of the needle during acupuncture delivery by collecting the movement trajectory of holder. The 3-month old male Bama miniature pig was selected as the experimental subject, and 6 points, "Bojian" "Qiangfeng" "Housanli" "Xiaokua" "Huiyang" (BL35) and "Baihui" (GV20), were selected during acupuncture manipulation. The optical navigation system was used to collect the real-time data, and these data were per-processed and analyzed using mean filtering and Fourier transform. The acupuncture procedure was divided into 3 stages, inserting, lifting-thrusting, and twisting. The results showed that the accuracy was 96.3% at lifting-thrusting stage, and that was 100.0% at twisting stage. The decomposition effect of the entire procedure was satisfactory. This study provides a new approach to the quantitative analysis of acupuncture manipulation. In the future, it needs to further optimize the algorithm and expand the sample size so as to improve the accuracy of this analytic technique.
Acupuncture Therapy/methods* ; Male ; Animals ; Swine ; Acupuncture Points ; Humans ; Swine, Miniature ; Needles

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To explore the advantages of robotic-arm assisted total hip arthroplasty (rTHA) in acetabular component positioning and lower limb length assessment in patients with severe pelvic coronal tilt.Methods:A retrospective analysis was conducted on the data of 122 patients with unilateral end-stage hip disease and coronal pelvic tilt angle >3° who underwent total hip arthroplasty (THA) at the First Medical Center of PLA General Hospital from June 2022 to December 2023. Among them, 44 patients underwent rTHA, and 78 underwent manual THA (mTHA). The rTHA group included 18 males and 26 females, with an average age of 60.5±9.3 years; the mTHA group included 41 males and 37 females, with an average age of 58.5±8.4 years. Compare the differences in the anteversion angle, abduction angle, pelvic tilt angle, leg length discrepancy (LLD) of the acetabular prosthesis, and the proportions of patients with LLD>0.5 cm and >1 cm between the two groups of patients after surgery. Calculate the proportion of outlier rates of acetabular abduction angle (<30° or >45°), and proportions within Callanan's safe zone. The early efficacy of the hip joint was evaluated by using the modified Harris score and joint range of motion.Results:All patients were followed up for 6 to 12 months, with an average of 8 months. All the surgical incisions of the patients achieved primary healing. Postoperative comparisons showed no statistically significant differences in acetabular abduction angle (39.5°±3.3° vs. 38.4°±7.3°) or anteversion angle (20.7°±1.6° vs. 19.7°±1.6°) between rTHA and mTHA groups ( P>0.05). However, pelvic tilt angle [2.5° (1.1°, 3.6°) vs. 3.5° (2.3°, 5.9°)] showed a statistically significant difference ( U=4.371, P=0.008). The rTHA group exhibited smaller absolute LLD [0.2 (0.1, 0.4) cm vs. 0.5 (0.2, 0.5) cm] and lower proportions of LLD >0.5 cm [14% (6/44) vs. 49% (38/78)] and >1 cm [2% (1/44) vs. 18% (14/78)], with statistical significance ( P<0.05). The rTHA group had a lower outlier rate for acetabular abduction angle (<30°or >45°) compared to the mTHA group [2% (1/44) vs. 33% (26/78)], with statistical significance (χ 2=10.388, P<0.001). Taking the Callanan safety zone as the standard, the proportion of acetabular cups within the safe zone was significantly higher in the rTHA group (98%, 43/44) compared to the mTHA group (67%, 52/78), with a statistically significant difference (χ 2=13.998, P<0.001). The modified Harris score and hip joint range of motion in the mTHA group increased from 47.6±6.6 points and 83° (73°, 88°) before the operation to 83.5±11.2 points and 118° (110°, 122°) at the last follow-up, respectively. The rTHA group increased from 46.5±9.2 points and 79° (71°, 90°) before the operation to 85.0±12.5 points and 124° (116°, 130°) at the last follow-up. The differences in the modified Harris score and hip joint range of motion between the two groups before the operation and at the last follow-up were statistically significant ( P<0.05). However, there was no statistically significant difference between the groups at the last follow-up ( P>0.05). No THA-related complications occurred during follow-up period. Conclusion:For patients with end-stage hip joint diseases with coronal tilt exceeding 3°, robotic-assisted technology significantly improves the accuracy of acetabular component placement during THA and offers better control of postoperative LLD.

Objective:To construct an infectious clone of a Chinese HIV-1 CRF01_AE epidemic strain with strong replication capacity，and comprehensively identify its viral phenotype and replication capacity.Methods:Using the CRF01_AE clinical isolate GX2005002，which was previously isolated from whole blood of an HIV-1-infected individual in China by our laboratory，as the parental strain，the full-length genome of the virus（9.7 kb）was divided into 5' half fragment（5.1 kb）and 3' half fragment（4.6 kb）for amplification. The proviral DNA was used as a template to amplify the virus genome，which was then ligated into the eukaryotic expression vector pEASY-T1. The consistency of its sequence with the parental strain sequence was identified through full-length genome sequencing and phylogenetic analysis. The replication capacity，syncytium inducibility，and cell tropism were experimentally identified to determine its phenotypic consistency with the parental strain.Results:An infectious clone of the CRF01_AE strain was successfully constructed，and its genome sequence exhibited high consistency with the sequence of the parental strain. By transfecting target cells，a derivative virus with infectious activity and replication capability was successfully rescued. The derived virus maintained phenotypic characteristics consistent with the parental strain，such as cell tropism and syncytium inducibility.Conclusion:This study successfully constructed an infectious clone of a Chinese HIV-1 CRF01_AE epidemic strain with clear background and distinct phenotype. The genomic sequence and viral phenotypic characteristics of the derived virus are basically consistent with the parental strain，providing strong representation of the original isolate and serving as a powerful tool for research on the correlation between the genetic characteristics，viral phenotype，and pathogenicity of HIV-1 CRF01_AE strains.

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

OBJECTIVES: To create a mixed valvular heart disease (MVHD)-related age-adjusted comorbidity index (MVACI) model for predicting mortality risk of patients with MVHD. METHODS: A total of 4080 patients with moderate or severe MVHD in the China-VHD study were included. The primary endpoint was 2-year all-cause mortality. A MVACI model prediction model was constructed based on the mortality risk factors identified by univariate and multivariate Cox regression analysis. Restricted cubic splines were used to assess the relationship between MVACI scores and 2-year all-cause mortality. The optimal threshold, determined by the maximum Youden index from receiver operator characteristic (ROC) curve analysis, was used to stratify patients. Kaplan-Meier method was used to calculate 2-year all-cause mortality and compared using the Log-rank test. Univariate and multivariate Cox proportional hazards models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI), evaluating the association between MVACI scores and mortality. Paired ROC curves were used to compare the discriminative ability of MVACI scores with the European System for Cardiac Operative Risk Evaluation Ⅱ(EuroSCORE Ⅱ) or the age-adjusted Charlson comorbidity index (ACCI) in predicting 2-year clinical outcomes, while calibration curves assessed the calibration of these models. Internal validation was performed using the Bootstrap method. Subgroup analyses were conducted based on etiology, treatment strategies, and disease severity. RESULTS: Multivariate analysis identified the following variables independently associated with 2-year all-cause mortality in patients: pulmonary hypertension, myocardiopathy, heart failure, low body weight (body mass index <18.5 kg/m²), anaemia, hypoalbuminemia, renal insufficiency, cancer, New York Heart Association (NYHA) class and age. The score was independently associated with the risk of all-cause mortality, and exhibited good discrimination (AUC=0.777, 95%CI: 0.755-0.799) and calibration (Brier score 0.062), with significantly better predictive performance than EuroSCORE Ⅱ or ACCI (both adjusted P<0.01). The internal validation showed that the MVACI model's predicted probability of 2-year all-cause mortality was generally consistent with the actual probability. The AUCs for predicting all-cause mortality risk were all above 0.750, and those for predicting adverse events were all above 0.630. The prognostic value of the score remained consistent in patients regardless of their etiology, therapeutic option, and disease severity. CONCLUSIONS The MVACI was constructed in this study based on age and comorbidities, and can be used for mortality risk prediction and risk stratification of MVHD patients. It is a simple algorithmic index and easy to use.
Humans ; Prognosis ; Comorbidity ; Heart Valve Diseases/epidemiology* ; Female ; Male ; Middle Aged ; Aged ; Proportional Hazards Models ; Risk Factors ; China/epidemiology* ; Age Factors ; Risk Assessment ; Adult ; ROC Curve

In order to study the pathogenesis of meningitis in lambs caused by Enterococcus faecalis(E.faecalis),and to elucidate the pathogenesis of meningitis from the circRNA level,E.faecalis(named XJ6)was reintroduced into the host to construct a lamb brain tissue injury model,and then the lamb brain tissues were selected for whole genome transcript sequencing at four time in-tervals(24,48,60 and 72 h).Based on the transcriptomics sequencing results,the differential cir-cRNAs were preliminarily screened,and the accuracy of the transcriptomics data was verified by real-time fluorescence quantification.The results showed that the experiments successfully con-structed the brain tissue injury model of E.faecalis infected lambs,and the GO enrichment analy-sis results showed the most significant 10 cellular components,10 biological processes,and 10 mo-lecular functions.KEGG pathway enrichment results revealed that the genes targeted by differenti-al circRNAs were mainly focused on signaling pathways regulating the neuronal cells,the MAPK signaling pathway,the Rap1 signaling pathway,VDEF signaling pathway,Apelin signaling path-way,vascular smooth muscle contraction,and cancer development.The differentially expressed cir-cRNAs validated by real-time quantitative PCR were completely consistent with the transcriptomic results.Among them,novel_circ_0004872 was identified as a candidate circRNA potentially involved in altering the permeability of the blood-brain barrier in lambs,providing a theoretical ref-erence for exploring the regulatory role of circRNAs in blood-brain barrier function.

Objectives:Utilizing an advanced and high-capacity data acquisition system tailored for elderly patients with valvular heart disease,we aimed to establish standardized data acquisition protocols and define variable sets for a clinical research big data platform dedicated to this condition.Guided by clinical treatment decisions,we implemented a standardized and uniform evaluation process for valvular heart disease.This initiative might culminate in the creation of the first comprehensive database for elderly valvular heart disease,encompassing clinical information,functional assessments,multimodal imaging,treatment modalities,and follow-up data.Methods:Variables were selected based on past clinical studies and literature,both domestic and international,and considering key guidelines,advancements in valvular heart disease,and China's clinical practices.Standards for data variables,imaging techniques,data acquisition,multi-channel verification,and quality control were established.Variables are then classified by their properties and generation time.Results:This comprehensive set of variables encompassed a total of 567 relevant variables,which were generated throughout the entire diagnosis and treatment process of patients with valvular heart disease,starting from admission and extending to those required for clinical research.The variable set was divided into six distinct parts:clinical information(encompassing demographics,admission details,clinical manifestations,comorbidities,medical history,physical examination,auxiliary examinations,and comprehensive assessment of frailty in the elderly),echocardiography,cardiac computed tomography(CT),treatment(including surgery,valve intervention therapy,non-valve intervention therapy,and medication therapy),clinical outcomes,and follow-up.Specifically,it included 14 variables related to demographic and admission information;22 variables encompassing clinical manifestations and physical examination;63 variables concerning medical history and comorbidities;49 variables for inspection and examination;15 variables for the comprehensive evaluation of frail elderly individuals;55 variables of echocardiography,including complete dynamic images at 4 time points,6 cross-sections per point,and corresponding Doppler spectra);131 variables for cardiac CT,including baseline and postoperative raw image uploads(encompassing non-enhanced electrocardiography[ECG]-gated cardiac CT scans,ECG-gated full-phase CT angiography(CTA)scans,and large-scale non-ECG-gated spiral CTA scans);51 variables for surgical diagnosis and treatment;72 variables for valve intervention therapy;33 variables for non-valvular interventional therapy;27 variables for medication information;and 35 variables for clinical outcomes.The follow-up integration comprises 140 variables,along with the upload of raw echocardiography and CT images.Conclusions:Through the establishment of data collection standards and variable standards for the big data platform of valvular heart disease,the standardization and normalization of comprehensive evaluation,diagnosis and treatment,and clinical research of patients with valvular heart disease can be achieved.As a national level big data research platform that can be sustainably used for clinical research and innovative development of valvular heart disease,it will provide support for long-term monitoring of valvular heart disease,real-world data analysis,clinical research(observational and randomized controlled trials),technological innovation,drug and device development,and post market evaluation.