Atrial fibrillation(AF)is the most common arrhythmia in clinical practice,with over 37 million people affected worldwide.AF may have great impacts on patients'cardiac function,cognitive function,quality of life,increase the risk of stroke,and raise the cardiovascular mortality.Therefore,early prediction of AF is crucial for reducing the risk of AF-related complications,early intervention,and improving patient prognosis.With the rapid development of artificial intelligence(AI)technology,its application in the medical field,especially in cardiology,has increasingly attracted great attention.In recent years,numerous studies have utilized various AI tools to analyze vast amounts of medical data,including clinical information and imaging parameters,trying to predict the occurrence,progression,and complications of AF,thereby providing a basis for clinical decision-making.However,despite certain progress,current research still faces multiple challenges,such as data privacy,model interpretability,the accuracy and rationality of parameter selection,and the clinical operability and feasibility of data implementation.Therefore,we aim to review the latest researches of AI in AF prediction,analyze the current application status,and discuss the future development,in order to provide references and insights for further research in related fields.

Atrial fibrillation(AF)is the most common arrhythmia in clinical practice,with over 37 million people affected worldwide.AF may have great impacts on patients'cardiac function,cognitive function,quality of life,increase the risk of stroke,and raise the cardiovascular mortality.Therefore,early prediction of AF is crucial for reducing the risk of AF-related complications,early intervention,and improving patient prognosis.With the rapid development of artificial intelligence(AI)technology,its application in the medical field,especially in cardiology,has increasingly attracted great attention.In recent years,numerous studies have utilized various AI tools to analyze vast amounts of medical data,including clinical information and imaging parameters,trying to predict the occurrence,progression,and complications of AF,thereby providing a basis for clinical decision-making.However,despite certain progress,current research still faces multiple challenges,such as data privacy,model interpretability,the accuracy and rationality of parameter selection,and the clinical operability and feasibility of data implementation.Therefore,we aim to review the latest researches of AI in AF prediction,analyze the current application status,and discuss the future development,in order to provide references and insights for further research in related fields.

Aim To study the effect of evodiamine(EVO)regulating forkhead box protein Ml(FOXM1)on the proliferation and apoptosis of colorectal cancer-resistant cells HCT8/5-FU.Methods CCK-8 assay and EdU assay were used to detect the effect of EVO on cell proliferation ability.Clone formation assay was employed to detect the effect of EVO on the clone for-mation ability of cells.Flow cytometric counting was applied to detect apoptosis.Western blot was utilized to detect the expression of cellular Bcl-2,Bax,FOXM1,β-catenin,c-MYC,and CyclinD1;Molecular docking was used to explore the EVO-FOXM1 interac-tion.Nude mouse transplant tumor model was estab-lished to validate the effect of EVO on HCT8/5-FU cells in vivo.Results CCK-8 assay showed that EVO inhibited the proliferation of HCT8/5-FU cells in a time-and concentration-dependent manner.EdU assay found that the newly proliferated cells in the EVO-trea-ted group were significantly reduced.The results of the clone formation assay showed that EVO inhibited the clone-forming ability of HCT8/5-FU cells.Flow cyto-metric counting found that apoptosis rate of the cells in the EVO group significantly increased.Western blot showed that FOXM1 and β-catenin were significantly highly expressed in HCT8/5-FU cells,and EVO down-regulated the expression of FOXM1,β-cateniin,c-MYC,CyclinD1,and Bcl-2,and up-regulated the ex-pression of Bax.Molecular docking revealed strong in-teractions between EVO and FOXM1.The in vivo ex-perimental results demonstrated that EVO exerted a substantial inhibitory effect on the growth of subcutane-ously implanted HCT8/5-FU xenograft tumors and regulated the expression of related proteins.HE stai-ning revealed significant nuclear consolidation and fragmentation of tumor cells in the EVO group.Con-clusions The findings suggest that EVO could sup-press the activation of the Wnt signaling pathway through a mechanism involving the downregulation of FOXM1 protein expression,thus inhibiting the prolifer-ation of HCT8/5-FU cells and induce their apoptosis.

Objective To construct a glioma grading evaluation model based on magnetic resonance diffusion tensor imaging(DTI)parameters,systemic immune-inflammation index(SII),and albumin to globulin ratio(AGR),and analyze its evaluation effect.Methods A total of 120 patients with brain glioma treated in our hospital from January 2022 to January 2024 were selected and divided into a high-grade glioma(HGG)group of 53 cases and a low-grade glioma(LGG)group of 67 cases according to the WHO tumor classification.The magnetic resonance DTI parameters,SII,and AGR of patients between the two groups were compared.A binary Logistic regression analysis model was constructed to analyze the risk factors for the occurrence of HGG;the receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to analyze the evaluation value of the glioma grading model.Results Compared with the LGG group,the HGG group had lower fractional anisotropy(FA)value,apparent diffusion coefficient(ADC),and AGR of the tumor body and the peritumoral edema area(P<0.05),and higher SII(P<0.05).Binary Logistic regression analysis showed that decresed ADC value of the tumor body and the peritumoral edema area,decreased AGR,and increased SII were all the risk factors for the occurrence of HGG(P<0.05).The ROC curve analysis showed that the AUC of the combined assessment of FA value and ADC of the tumor body,FA value and ADC of the peritumoral edema area,SII and AGR for glioma grading was higher than that of their individual assessment(P<0.05).Conclusion The occurrence of HGG is related to the ADC of the tumor body and peritumoral edema area,SII,and AGR.The model constructed based on magnetic resonance DTI parameters,SII and AGR has high evaluation value for the grading of glioma.

Objective To construct a glioma grading evaluation model based on magnetic resonance diffusion tensor imaging(DTI)parameters,systemic immune-inflammation index(SII),and albumin to globulin ratio(AGR),and analyze its evaluation effect.Methods A total of 120 patients with brain glioma treated in our hospital from January 2022 to January 2024 were selected and divided into a high-grade glioma(HGG)group of 53 cases and a low-grade glioma(LGG)group of 67 cases according to the WHO tumor classification.The magnetic resonance DTI parameters,SII,and AGR of patients between the two groups were compared.A binary Logistic regression analysis model was constructed to analyze the risk factors for the occurrence of HGG;the receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to analyze the evaluation value of the glioma grading model.Results Compared with the LGG group,the HGG group had lower fractional anisotropy(FA)value,apparent diffusion coefficient(ADC),and AGR of the tumor body and the peritumoral edema area(P<0.05),and higher SII(P<0.05).Binary Logistic regression analysis showed that decresed ADC value of the tumor body and the peritumoral edema area,decreased AGR,and increased SII were all the risk factors for the occurrence of HGG(P<0.05).The ROC curve analysis showed that the AUC of the combined assessment of FA value and ADC of the tumor body,FA value and ADC of the peritumoral edema area,SII and AGR for glioma grading was higher than that of their individual assessment(P<0.05).Conclusion The occurrence of HGG is related to the ADC of the tumor body and peritumoral edema area,SII,and AGR.The model constructed based on magnetic resonance DTI parameters,SII and AGR has high evaluation value for the grading of glioma.

Aim To study the effect of evodiamine(EVO)regulating forkhead box protein Ml(FOXM1)on the proliferation and apoptosis of colorectal cancer-resistant cells HCT8/5-FU.Methods CCK-8 assay and EdU assay were used to detect the effect of EVO on cell proliferation ability.Clone formation assay was employed to detect the effect of EVO on the clone for-mation ability of cells.Flow cytometric counting was applied to detect apoptosis.Western blot was utilized to detect the expression of cellular Bcl-2,Bax,FOXM1,β-catenin,c-MYC,and CyclinD1;Molecular docking was used to explore the EVO-FOXM1 interac-tion.Nude mouse transplant tumor model was estab-lished to validate the effect of EVO on HCT8/5-FU cells in vivo.Results CCK-8 assay showed that EVO inhibited the proliferation of HCT8/5-FU cells in a time-and concentration-dependent manner.EdU assay found that the newly proliferated cells in the EVO-trea-ted group were significantly reduced.The results of the clone formation assay showed that EVO inhibited the clone-forming ability of HCT8/5-FU cells.Flow cyto-metric counting found that apoptosis rate of the cells in the EVO group significantly increased.Western blot showed that FOXM1 and β-catenin were significantly highly expressed in HCT8/5-FU cells,and EVO down-regulated the expression of FOXM1,β-cateniin,c-MYC,CyclinD1,and Bcl-2,and up-regulated the ex-pression of Bax.Molecular docking revealed strong in-teractions between EVO and FOXM1.The in vivo ex-perimental results demonstrated that EVO exerted a substantial inhibitory effect on the growth of subcutane-ously implanted HCT8/5-FU xenograft tumors and regulated the expression of related proteins.HE stai-ning revealed significant nuclear consolidation and fragmentation of tumor cells in the EVO group.Con-clusions The findings suggest that EVO could sup-press the activation of the Wnt signaling pathway through a mechanism involving the downregulation of FOXM1 protein expression,thus inhibiting the prolifer-ation of HCT8/5-FU cells and induce their apoptosis.

Objective:To evaluate the value of spectral CT quantitative parameters in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:A total of 100 HCC patients who underwent surgical resection and were pathologically diagnosed in the Affiliated Cancer Hospital of Xiangya Medical College of Central South University from January 2020 to January 2023 were retrospectively enrolled. According to pathological grading, the patients were divided into the microvascular invasion group (invasion group, n=60) and the non-vascular invasion group (non-invasion group, n=40). Serological indicators and spectral CT quantitative parameters were compared between the two groups. Receiver operating characteristic (ROC) curve was used to analyze the value of spectral CT quantitative parameters in predicting MVI of HCC. Results:The serum alpha-fetoprotein (AFP) level in the invasion group was higher than that in the non-invasion group, with a statistically significant difference ( P<0.05). There were no statistically significant differences in serum carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA-199) levels between the two groups (all P>0.05). In the invasion group, arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope were all higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there were no statistically significant differences in venous phase iodine concentration, venous phase normalized iodine concentration, and venous phase effective atomic number between the two groups (all P>0.05). The rates of peritumoral enhancement in the arterial phase and irregular tumor margin in the invasion group were higher than those in the non-invasion group, with statistically significant differences (all P<0.05); there was no statistically significant difference in tumor capsule between the two groups ( P>0.05). ROC curve analysis showed that the areas under the curve (AUC) of arterial phase iodine concentration, arterial phase normalized iodine concentration, venous phase iodine uptake reduction rate, arterial phase effective atomic number, and energy spectrum curve slope for predicting MVI in HCC were 0.812, 0.885, 0.726, 0.823, and 0.788, respectively. Conclusions:Spectral CT quantitative parameters are helpful to improve the preoperative diagnostic efficiency of MVI in HCC and can effectively predict MVI in HCC. Especially, arterial phase normalized iodine concentration has high application value in judging whether there is MVI in HCC.

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.