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.

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.

Objective:To plot Kaplan-Meier curves using simulated survival data and compare the characteristics of different survival probability extraction methods for Kaplan-Meier curves and their performance across various scenarios.Methods:Survival datasets were simulated using R-4.4.2 with parameters including sample sizes,censoring marker,and curve numbers.GetData Graph Digitizer,IPDfromKM getpoints,SurvdigitizeR survival_digitize were evaluated.A JavaScript script was developed to extract Kaplan-Meier curve.Root Mean Square Error(RMSE)was calculated to quantify deviations between digitized and true survival probabilities.Results:The JavaScript script method demonstrated the smallest RMSE across all simulated scenarios(RMSE=1.015×10-4),significantly outperforming the other three methods(P＜0.05),with statistically significant differences observed among methods(P＜0.05).Conclusion:For vector format illustrations,JavaScript scripts enable accurate and robust reverse engineering of Kaplan-Meier curves;for bitmaps,the GetData Graph Digitizer and SurvdigitizeR survival_digitize methods yield more accurate results,and the SurvdigitizeR survival_digitize method is the most efficient.Future research should focus on integrating intelligent algorithms for enhanced robustness and precision in survival data reconstruction.

With the progress of medical insurance reform,the refinement of medical insurance management in pub-lic hospitals still fails to meet the actual demands for medical insurance work.Based on the existing problems of medical insurance management,it emphasizes the necessity of the refined management of medical insurance.By integrating be-havioral economics theory,it divides the refined management of medical insurance into five distinct stages:develop-ment planning,process-oriented platform,organizational framework,staff training programs and regulatory supervi-sion.The behavioral logic of the refined management of medical insurance in public hospitals is analyzed.Building on this analysis,the relevant key insights are summarized to provide a reference for promoting the public welfare-oriented reform of public hospitals and realizing the high-quality development of public hospitals.

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 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.

From the perspective of competitive endogenous RNA(ceRNA) constructed by metastasy-associated lung adenocarcinoma transcript 1(Malat1) and microRNA 16-5p(miR-16-5p), the improvement mechanism of Tonggu Xiaotong Capsules(TGXTC) on the imbalance and disorder of "cholesterol-iron" metabolism in chondrocytes of osteoarthritis(OA) was explored. In vivo experiments, 60 8-week-old C57BL/6 mice were acclimatized and fed for 1 week and then randomly divided into two groups: blank group(12 mice) and modeling group(48 mice). The animals in modeling group were anesthetized by 5% isoflurane inhalation, which was followed by the construction of OA model. They were then randomly divided into model group, TGXTC group, Malat1 overexpression group, and TGXTC+Malat1 overexpression(TGXTC+Malat1-OE) group, with 12 mice in each group. The structural changes of mouse cartilage tissues were observed by Masson staining after the intervention in each group. RT-PCR was employed to detect the mRNA levels of Malat1 and miR-16-5p in cartilage tissues. Western blot was used to analyze the protein expression of ATP-binding cassette transporter A1(ABCA1), sterol regulatory element-binding protein(SREBP), cytochrome P450 family 7 subfamily B member 1(CYP7B1), CCAAT/enhancer-binding protein homologous protein(CHOP), acyl-CoA synthetase long-chain family member 4(ACSL4), and glutathione peroxidase 4(GPX4) in cartilage tissues. In vitro experiments, mouse chondrocytes were induced by thapsigargin(TG), and the combination of Malat1 and miR-16-5p was detected by double luciferase assay. The fluorescence intensity of Malat1 in chondrocytes was determined by fluorescence in situ hybridization. The miR-16-5p inhibitory chondrocyte model was constructed. RT-PCR was used to analyze the levels of Malat1 and miR-16-5p in chondrocytes under the inhibition of miR-16-5p. Western blot was adopted to analyze the regulation of TG-induced chondrocyte proteins ABCA1, SREBP, CYP7B1, CHOP, ACSL4, and GPX4 by TGXTC under the inhibition of miR-16-5p. The results of in vivo experiments showed that,(1) compared with model group, TGXTC group exhibited a relatively complete cartilage layer structure. Compared with Malat1-OE group, TGXTC+Malat1-OE group showed alleviated cartilage surface damage.(2) Compared with model group, TGXTC group had a significantly decreased Malat1 mRNA level and an increased miR-16-5p mRNA level in mouse cartilage tissues(P<0.01).(3) Compared with the model group, the protein levels of ABCA1 and GPX4 in the cartilage tissue of mice in the TGXTC group increased, while the protein levels of SREBP, CYP7B1, CHOP and ACSL4 decreased(P<0.01). The results of in vitro experiments show that,(1) dual-luciferase was used to evaluate that miR-16-5p has a targeting effect on the Malat1 gene.(2)Compared with TG+miR-16-5p inhibition group, TG+miR-16-5p inhibition+TGXTC group had an increased mRNA level of miR-16-5p and an decreased mRNA level of Malat1(P<0.01).(3) Compared with TG+miR-16-5p inhibition group, TG+miR-16-5p inhibition+TGXTC group exhibited increased expression of ABCA1 and GPX4 proteins and decreased expression of SREBP, CYP7B1, CHOP, and ACSL4 proteins(P<0.01). The reasults showed that TGXTC can regulate the ceRNA of Malat1 and miR-16-5p to alleviate the "cholesterol-iron" metabolism disorder of osteoarthritis chondrocytes.
Animals ; MicroRNAs/metabolism* ; RNA, Long Noncoding/metabolism* ; Chondrocytes/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Mice, Inbred C57BL ; Mice ; Osteoarthritis/drug therapy* ; Iron/metabolism* ; Male ; Cholesterol/metabolism* ; Humans ; Capsules ; RNA, Competitive Endogenous

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.