ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

Objective To explore the effect of the knockdown of transmembrane 9 superfamily protein member 2 (TM9SF2) on the replication of vesicular stomatitis virus (VSV), and investigate its role in the mechanism of antiviral innate immunity. Methods Small interfering RNA (siRNA) was used to knock down the TM9SF2 gene in human non-small cell lung cancer A549 cells. The CCK-8 method was used to assess cell proliferation. A VSV-green fluorescent protein (VSV-GFP) infected cell model was established. The plaque assay was used to measure the viral titer in the supernatant. RT-qPCR and Western blotting were employed to quantify the mRNA and protein levels of VSV genome replication in A549 cells following VSV infection, as well as the expression of interferon β (IFN-β) mRNA and interferon regulatory factor 3 (IRF3) protein phosphorylation following polyinosinic-polycytidylic acid (poly(I:C)) stimulation. Results Compared to the negative control, the knockdown of TM9SF2 exhibited a significant effect, with no observed impact on A549 cell proliferation. The VSV-GFP infected A549 cell model was successfully established. After viral stimulation, fluorescence intensity was reduced following TM9SF2 knockdown, and the mRNA and protein levels of VSV were significantly downregulated. The viral titer of VSV was decreased. After poly(I:C) stimulation, TM9SF2 knockdown significantly upregulated the mRNA level of IFN-β and the phosphorylation level of IRF3 protein. Conclusion The knockdown of TM9SF2 inhibits the replication of vesicular stomatitis virus, and positively regulates the type I interferon signaling pathway, thus enhancing the host's antiviral innate immune response.
Humans ; Virus Replication/genetics* ; Signal Transduction ; Membrane Proteins/metabolism* ; A549 Cells ; Vesiculovirus/physiology* ; Interferon-beta/metabolism* ; Interferon Regulatory Factor-3/genetics* ; Interferon Type I/metabolism* ; Vesicular Stomatitis/immunology* ; Gene Knockdown Techniques ; Vesicular stomatitis Indiana virus/physiology* ; RNA, Small Interfering/genetics*

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Hereditary angioedema (HAE) is a rare genetic disorder, typically managed with on-demand medications during acute attacks. Clinical trials and real-world studies have demonstrated the efficacy and safety of icatibant in treating acute HAE attacks. This article aims to summarize research findings on icatibant in HAE treatment, providing a reference for its clinical application.

Enamel demineralization, the formation of white spot lesions, is a common issue in clinical orthodontic treatment. The appearance of white spot lesions not only affects the texture and health of dental hard tissues but also impacts the health and aesthetics of teeth after orthodontic treatment. The prevention, diagnosis, and treatment of white spot lesions that occur throughout the orthodontic treatment process involve multiple dental specialties. This expert consensus will focus on providing guiding opinions on the management and prevention of white spot lesions during orthodontic treatment, advocating for proactive prevention, early detection, timely treatment, scientific follow-up, and multidisciplinary management of white spot lesions throughout the orthodontic process, thereby maintaining the dental health of patients during orthodontic treatment.
Humans ; Consensus ; Dental Caries/etiology* ; Dental Enamel/pathology* ; Tooth Demineralization/etiology* ; Tooth Remineralization

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child