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.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

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.

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.

Objective To explore a nomogram of intratumor and peritumor radiomics based on enhanced CT venous phase to pre-operatively predict the pathological grade of poorly differentiated esophageal squamous cell carcinoma(ESCC).Methods A retro-spective selection was made of 266 ESCC patients confirmed by pathology(76 cases of poorly differentiated;190 cases of non-poorly differentiated),and all patients were randomly divided into training set(n=186),validation set(n=80),and full data set(n=266).Tumors were segmented on the enhanced CT venous phase to create three-dimensional region of interest(ROI)of intratumor,peritu-mor 0.3 cm,and intratumor+peritumor 0.3 cm.A total of 2 553 radiomics features were extracted.After feature dimensionality reduc-tion,XGboost machine learning algorithm was utilized to rank the top fifteen features.Stepwise forward multiple logistic regression was employed to identify the most significant features.The radiomics scores of the intratumor,peritumor 0.3 cm,and intratumor+peritu-mor 0.3 cm were calculated.The diagnostic efficacy of the model was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results The nomogram constructed based on radiomics scores of intratumor,peritumor 0.3 cm,intratumor+peritumor 0.3 cm in the training set for preoperative prediction of poorly differentiated ESCC had an AUC of 0.899[95％confidence interval(CI)0.846-0.938],and it was well validated in the vali-dation set(AUC 0.869,95％CI 0.775-0.934)and the full data set(AUC 0.889,95％CI 0.845-0.924).Additionally,calibration curves and DCA indicated that the nomogram achieved good calibration ability in the three cohorts and offered greater clinical net benefit.Conclusion The nomogram based on enhanced CT venous phase intratumor and peritumor radiomics achieves a high and stable diagnostic efficacy for preoperatively predicting poorly differentiated ESCC,which may help with individualized surgical selec-tion and management before surgery.

Objective:To explore the influence of initial high-risk cytogenetic abnormalities on the outcomes of children with acute myeloid leukemia (AML) after post-transplant Cyclophosphamide (PTCy)-based allogeneic hematopoietic stem cell transplantation (allo-HSCT).Methods:A retrospective cohort study.AML children who underwent PTCy-based allo-HSCT after the first complete remission at Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science and Technology between April 2017 and April 2024 were enrolled.Patients were divided into intermediate-risk and high-risk groups based on their initial cytogenetic features.These patients were further divided into complex karyotype, 11q23 rearrangement, and other karyotype groups.Clinical characteristics and survival outcomes were compared among these groups.Measurement and count data were analyzed using Wilcoxon rank-sum/Kruskal-Wallis and χ2 tests, respectively.Survival and risk factor analyses were performed using Kaplan-Meier and Cox proportional hazards methods, respectively. Results:A total of 51 AML children who underwent allo-HSCT were included in this study.The median age at transplantation was 3.2 years and the median follow-up time was 4.6 years.There were 26 cases in the intermediate-risk group and 25 cases in the high-risk group; 8 cases in the complex karyotype group, 14 cases in the 11q23 rearrangement group, and 29 cases in the other karyotype groups.By the end of the follow-up on November 30, 2024, 11 patients relapsed, 8 patients died, and 13 patients developed grades Ⅱ-Ⅳ acute graft-versus-host disease (GVHD).The 3-year overall survival (OS), relapse-free survival (RFS), and grades Ⅱ-Ⅳ acute GVHD-free and relapse-free survival (GRFS) were 84.0% (95% CI: 74.4%-94.8%), 74.5% (95% CI: 63.4%-87.5%), and 58.8% (95% CI: 46.7%-74.0%), respectively.The 3-year OS of the high-risk group was significantly lower than that of the intermediate-risk group (71.8% vs.96.2%, P=0.022), while differences in 3-year RFS and GRFS between the 2 groups were not statistically significant (68.0% vs.80.8%, P=0.400; 52.0% vs.65.4%, P=0.420).The 3-year OS, RFS and GRFS of the complex karyotype group were significantly lower than those of 11q23 rearrangement and other karyotype groups (50.0% vs.85.7%, 93.1%, P=0.009; 37.5% vs.85.7%, 79.3%, P=0.022; 25.0% vs.64.3%, 65.5%, P=0.049).Multivariate analysis showed that a complex karyotype was an independent prognostic factor affecting 3-year OS and GRFS [OS: HR=6.79 (95% CI: 1.13-43.80), P=0.044; GRFS: HR=3.72(95% CI: 1.13-12.20), P=0.030]. Conclusions:High-risk cytogenetic features are significant predictors of survival outcomes in pediatric AML patients undergoing PTCy-based allo-HSCT.

Objective To design a performance testing platform to evaluate the working status and performance characteristics of the ventilator proportional solenoid valve.Methods The performance testing platform had its hardware including a high-pressure gas source,a pressure regulating valve,sensors and etc,and its software designed based on PyQt5 and composed of several modules for data acquisition,parameter setting,image display,indicator computation,result output and etc.Two kinds of proportional solenoid valves(Valve 1、Valve2)were selected for static and dynamic tests to verify the performance of the platform.Results The platform developed facilitated the proportional solenoid valve to carry out accurate computation of static and dynamic indicators at real time and time domain and waveform feature extraction of sensor data by precision control and data acquisition for the proportional solenoid valve.Static tests showed that Valve 1 gained advantages over Valve 2 in static flow characteristics involving in lowered repeatability,return error and offset while enhanced stability;dynamic tests indicated Valve 2 had rapid flow variations and significant flow fluctuation impacts,Valve 1 showed smooth dynamic response changes,and Valve 2 behaved better than Valve 1 in dynamic performance.Conclusion The testing platform developed comprehensively demonstrates the performance characteristics and working performance of the ventilator proportional solenoid valve,which is of great significance to enhance the reliability and safety of the ventilator.[Chinese Medical Equipment Journal,2025,46(1):13-19]

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.

Aortic regurgitation and mitral regurgitation are more common in elderly heart valve disease,and both may be present in some patients.Severe aortic regurgitation complicated with severe mitral regurgitation often requires surgical valve replacement,but in patients at high risk of surgery,the risk of perioperative mortality is significantly increased.Therefore,for such patients,minimally invasive interventions can significantly improve long-term patient outcomes while reducing surgical risk.This article report a case of transcatheter aortic valve replacement combined with transcatheter edge-to-edge repair in the treatment of severe aortic regurgitation combined with mitral valve prolapse,in order to explore new treatment ideas for similar cases.

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.