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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective:To investigate the protection effect of a new 360-degree radiation protection and position fixation device on the pelvic cavity during chest CT examination.Methods:Three shielding methods were applied to the pelvic cavity of the standard simu-lated human model,i.e.,no shielding(group A),traditional 180-degree front protection with a lead square towel(group B),and 360-degree protection with a new protection device(group C).Philips IQon Spectral CT was used to perform chest CT scan at a tube voltage of 80 kVp,100 kVp,and 120 kVp,respectively,and the cumulative radiation doses from the front,side,and back of the pelvic cavity were measured and analyzed statistically.Results:Compared with groups A and B,group C had significantly lower cumulative radiation doses of the front,side,and back of the pelvic cavity in the simulated human model(all P<0.05);at the tube voltages of 80,100,and 120 kVp,the cumulative dose of the pelvic cavity in group C was reduced by 85%,84%,and 67%,respectively,compared with that in group B,and was reduced by 88%,87%,and 76%,respectively,compared with that in group A.Compared with group A,group B had significant reductions in the radiation doses of the side and back of the pelvic cavity(P<0.05)and a significantly higher radiation dose of the front of the pelvic cavity,which was increased by 19%,23%,and 10%,respectively,at the tube voltages of 80,100,and 120 kVp,and there were significant differences in all tube voltage conditions(P<0.05)except under the tube voltage condition of 120 kVp(P=0.190).In addition,after the application of the device for protection,the reduction rate of pelvic radiation dose under the tube voltages of 80 and 100 kVp was higher than that under the tube voltage of 120 kVp.Conclusion:The new 360-degree radiation protection and position fixation device can significantly reduce the cumulative radiation dose of pelvic organs during chest CT scan,and it holds promise for clinical application due to its characteristics of conve-nient wearing and fixed patient position.

Objectives:The aim is to analyze the cost structure and coefficient of variation for hospitalized patients with malnutrition based on Diagnosis-Related Groups(DRG),providing a reference for the further application and promotion of DRG.Method:Data were collected from patients admitted to Ningxia Hui Autonomous Region People's Hospital between March 2023 and August 2023.A diagnostic system based on artificial intelligence was used to identify malnourished patients.The composition of hospitalization costs for these individuals was described and analyzed,as was the coefficient of variation for various costs within DRG groupings.A multivariate regression analysis was conducted to identify the factors that influence patient hospitalization costs.Results:The average age of hospitalized patients with malnutrition was(68.12±16.43)years,with an average length of stay of(14.55±8.47)days,with an average hospitalization cost of(32 128.89±35 345.61)yuan.Among patients within the same DRG group,the coefficient of variation for various costs was found to be lower in the malnutrition group than in the normal group.This suggests that when assessed individually,the malnutrition group exhibited a higher degree of homogeneity in their cost structures.The factors influencing total hospitalization costs were found to be:length of hospital stay(P=0.001),nutritional monitoring fees(P=0.020),number of chronic diseases(P=0.003),and Karnofsky Performance Status(KPS)score(P=0.038).Hospitalization costs were positively correlated with both length of stay and nutritional assessment fees,but negatively correlated with the number of chronic diseases and KPS scores.Conclusions:Malnutrition has a profound impact on health outcomes,medical expenses,length of hospital stay,and disease severity.The implementation of the DRG system aims to standardize and improve the nutritional diagnosis and treatment process by categorizing different stages of malnutrition.This approach can minimize variations within DRG groups,making it easier to allocate medical resources more precisely and efficiently.Furthermore,it is a valuable reference tool for promoting DRG payment reform in different regions.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To analyze the publication condition and development trends of global post-stroke fatigue(PSF)research from 2004 to 2024.Methods Relevant literature on PSF pub-lished between January 1,2004,and August 1,2024,were systematically searched and screened from WoSCC database.Visual analysis of publication volume,contributing countries,institutions,authors,and keywords in PSF research was conducted using CiteSpace software.Results A total of 373 articles were included in this study.The overall publication volume related to PSF exhibited an upward trend.The country,institution,and author with the highest number of publications were the United Kingdom,the University of Oslo,and LERDAL A,respectively.High-frequency keywords in-cluded quality of life,depression,fatigue severity scale,follow up,epidemiology,symptoms,associ-ations,and rehabilitation,while the emerging keywords were health care professionals,risk factors,and participation.Conclusion PSF-related research has demonstrated an overall upward trend.As-sessment and measurement protocols,rehabilitation treatment approaches,impacts on quality of life,and the intrinsic relationship with depression have emerged as research hotspots.Further exploration of precise and systematic assessment methods,along with the construction of scientific rehabilitation management programs and risk prediction models,will become the focal points of future research.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

OBJECTIVE To explore the effect of failure mode and effect analysis(FMEA)on management of hospi-tal-associated infections(HAIs)in hemodialysis center.METHODS In Nov.2023,the risk priority number(RPN)integrated with action priority(AP)was adopted to identify,analyze and evaluate the risk factors in man-agement of HAIs in hemodialysis center of Tongji Hospital Affiliated to Tongji Medical College,Huazhong Uni-versity of Science and Technology by FMEA method.The high risk points that needed to be taken interventions were screened out,and the targeted measures were formulated to control the risks.At the end of the intervention period,a second round of risk assessment was carried out for improvement status of the high-risk points in Nov.2024,and the effect on the management of HAIs was evaluated.RESULTS The risk assessment was carried out for 48 risk points covering eight aspects,including organizational structure,self-inspection and supervision,staff management,environmental layout,cleaning and disinfection,surveillance,operation procedures and i-tem management.There were 9 risk points with the RPN values greater than 125,3 of which were with the AP value of"H".There were 8 risk points with the RPN value less than 125 and 6 risk points with the AP value drop-ping down to L after the targeted intervention measures were taken,indicating that the risk management has a-chieved favorable effect.CONCLUSIONS The RPN and AP integrated with FMEA can accurately identify the high-risk points in the quality management of the hemodialysis center.It is necessary to take targeted interven-tion measures so as to boost the effect on prevention and control of HAIs in the hemodialysis center and reduce the risk of HAIs in the hemodialysis patients.