OBJECTIVE To investigate the protective effects and potential mechanism of alisol B 23-acetate on acute alcoholic liver injury in mice. METHODS Fifty male Kunming mice were divided into the blank group， model group， and alisol B 23-acetate low-， medium- and high-dose groups （10， 20， 40 mg/kg）， with 10 mice in each group. Each group was given relevant drug solution or normal saline intragastrically， once a day， for 2 consecutive weeks. On the 15th day， mice in the blank group were given normal saline intragastrically， while the other four groups were given 12 mL/kg white wine intragastrically， twice at six-hour intervals， to establish an acute alcoholic liver injury model. On the 16th day of the experiment， the liver indexes of mice in each group were calculated； the serum levels of alanine transaminase （ALT）， aspartate transaminase （AST）， total cholesterol （TC）， triglycerides （TG）， malondialdehyde （MDA） and glutathione （GSH） were also determined. The histopathological morphology of their liver tissues was observed and scored. The protein expressions of cytochrome P450 2E1 （CYP2E1）， Kelch-like ECH-associated protein 1 （Keap1）， nuclear factor erythroid 2-related factor 2 （Nrf2） and NAD（P）H： quinone oxidoreductase 1 （NQO1） were measured in liver tissue. RESULTS Compared with model group， mice in each dosage group of alisol B 23-acetate showed varying degrees of recovery in body weight， along with improvements in pathological changes in liver tissues such as inflammatory cell infiltration and fatty vacu oles. Their liver indexes， histopathological scores of liver tissue， serum levels of ALT， AST， TC， TG and MDA， as well as the protein expressions of CYP2E1 and Keap1 in liver tissue， were all significantly decreased （ P ＜0.05 or P ＜0.01）. The serum GSH levels and the protein expressions of Nrf2 （except for the alisol B 23-acetate low-dose group） and NQO1 in liver tissue were significantly increased （ P ＜0.05 or P ＜0.01）， and the changes in the above quantitative indicators showed a dose-dependent pattern. CONCLUSIONS Alisol B 23-acetate can ameliorate acute alcoholic liver injury in mice， and its mechanism may be related to improving antioxidant capacity by regulating the Keap1/Nrf2/NQO1 signaling pathway while simultaneously improving liver lipid metabolism-related indexes.

ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

Background: and Purpose In patients with large vessel occlusion (LVO), intravenous thrombolysis (IVT) frequently alters thrombus location; however, the clinical impact of this phenomenon remains unclear. We aimed to compare post-IVT thrombus dynamics between tenecteplase and alteplase and to evaluate the association between thrombus dynamics and 3-month outcomes. Methods: This retrospective study analyzed prospectively collected, multicenter data from consecutive patients with LVO who underwent bridging therapy between January 2022 and December 2024. Thrombus dynamics were classified as resolution, migration, or stability. Analyses incorporated propensity score matching with weighting to balance baseline characteristics. Results: Of the 806 initially included patients, 746 were included after matching (373 treated with tenecteplase and 373 treated with alteplase). The incidence of thrombus migration was significantly higher in the tenecteplase group than in the alteplase group (19.3% vs. 11.3%; odds ratio [OR]: 1.92; 95% confidence interval [CI] 1.27–2.91). The advantage of tenecteplase over alteplase was restricted to patients with an IVT-to-puncture time of <60 minutes (18.6% vs. 6.2%; p=0.001) and was no longer significant when the interval ≥60 minutes (19.7% vs. 15.0%; p=0.204; pinteraction=0.043). Additionally, thrombus migration was associated with a better functional outcome (OR: 1.62; 95% CI 1.04–2.53). Finally, tenecteplase was associated with improved functional independence compared with alteplase (OR: 1.43; 95% CI 1.04–1.95). Conclusions Tenecteplase demonstrated superior efficacy in inducing thrombus migration compared with alteplase, particularly within 60 minutes of IVT administration. Thrombus migration independently predicted improved functional independence. These findings support the preferential use of tenecteplase for bridging therapy in patients with LVO.

Exportin 1 (XPO1) is aberrantly overexpressed in various malignant tumors and can lead to the loss of anti-tumor effects of important tumor suppressor proteins such as p53, RB1, and FOXO by mediating their nuclear export. Although XPO1 inhibitor Selinexor has entered clinical application, its single-agent anti-tumor activity remains suboptimal, which is closely related to the compensatory activation of multiple signaling pathways in response to XPO1 inhibition. Focusing on the core regulatory role of XPO1 in tumor cells, this article systematically summarizes the current landscape of combination therapies involving XPO1 inhibitors and various targeted agents, including inhibitors of CDK4/6, FLT3, BET, ATR, and BCL2/MDM2, aiming to provide some reference for the development of XPO1-centered combination therapy strategies.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Introduction: The nutritional status of children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Down Syndrome (DS) is influenced by multiple food environment determinants. However, the precise relationship between food environment determinants and nutritional outcomes in these children remains unclear. This study aimed to understand the food environment determinants that are associated with the intake and nutritional status of these children in Malaysia, Lao PDR, and Indonesia. Methods: This cross-sectional study involved 7- to 18-year-old students with ADHD and DS in three Southeast Asian countries. Sociodemographic characteristics, dietary habits, and repeated 24-hour food recall data were collected through a structured questionnaire. Anthropometric data were obtained through direct measurements and analysed using the WHO AnthroPlus software. Further analyses were done using descriptive statistics and chi-square tests. Results: Total participants were 284 children, comprised of 37.7% with ADHD and 62.3% with DS; 62.3% of the children were boys and 37.7% were girls. Among the food environment determinants collected during the study, monthly household income and school food assistance status had significant associations (p<0.05) with BMI-for-age z-score category and total energy, protein, zinc, and calcium intakes. While the country, type of residence, and type of school had significant associations with total energy, protein, zinc, and calcium intakes. Conclusions: Food environment determinants, particularly household income and access to school food assistance, significantly influence the nutritional status and intake of children with ADHD and Down Syndrome. Thus, strengthening inclusive nutrition programmes may improve outcomes for this vulnerable group in Southeast Asia.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

BACKGROUND:With the increasing degree of population aging in China,the incidence of osteoporosis is rising annually.This growing demand for screening and diagnosis poses significant challenges to the healthcare system,increasing the time costs,financial burdens,and radiation exposure risks for patients.OBJECTIVE:To develop a novel interpretable prediction method based on traditional CT examination data and demographic data,aiming to reduce the number of patient examinations and enable multiple screenings from one examination.METHODS:A two-stage interpretable framework for osteoporosis prediction was designed.In the first stage,a human-computer collaborative method was used for annotating CT images,with an innovative vertebra 7-point CT value measurement technique.Patient's sex and age were used as key demographic features to enrich the model's input.In the second stage,the LightGBM model was enhanced by SHapley Additive exPlanations for quantitative analysis of feature importance,improving the interpretability of predictions and increasing clinical trust.Systematic experiments validated the effectiveness of the framework and the stability of the optimal feature set through the comparative analysis of different feature combinations with six machine learning models.To further assess the generalization ability of the model,the model was further tested on an external dataset.RESULTS AND CONCLUSION:The experiment compared six machine learning models suitable for medical applications,and the results showed that LightGBM model achieved an F1 score of 0.902 2 and an area under the curve of 0.938 7,outperforming the other models.In terms of interpretability,the clinical application credibility and operability of the model was increased by ranking and visualizing the contribution of input features to the results.Additionally,this study realized a prototype system,and testing results indicated that the system is user-friendly,capable of quickly processing data to provide prediction results,with visualized outcomes demonstrating good interpretability.This system effectively assists doctors in clinical decision-making and provides robust support for the screening and preliminary diagnosis of osteoporosis.

Drynaria fortunei,commonly known as"bone setting herb",has been widely included in various traditional Chinese herb-al classics for treating bone injuries.It is used medicinally from its rhizome,which has a bitter taste and warm property.It is known to nourish the kidneys,strengthen bones,and alleviate pain from injuries.The chemical constituents mainly include flavonoids,phenylpro-panoids,triterpenoids,phenolic acids,lignans,and sterols.Modern medical research indicates that Drynaria fortunei has anti-osteoporo-sis effects,promotes fracture healing,has anti-inflammatory properties,and benefits dental health.This article reviews the historical use of Drynaria fortunei and recent research on its chemical composition and pharmacological effects,summarizing some of the mechanisms of action.The aim is to provide a reference for further research on this medicinal herb.

Objective To investigate the value of intravoxel incoherent motion diffusion-weighted imaging(IVIM-DWI)in predicting the clinical and pathological features of prostate cancer(PCa).Methods We recruited 47 patients who underwent bpMRI combined with IVIM-DWI in our hospital from July 2022 to October 2023 and pathologically confirmed with PCa.Among these cases,20 were transitional zone PCa(TZ-PCa),and 27 were peripheral zone PCa(PZ-PCa).According to the International Society of Urological Pathology(ISUP)risk grades,the patients were divided into high-risk group(ISUP≥3)and low-risk group(ISUP≤2).Differences in the risk levels between TZ-PCa group and PZ-PCa group were compared.Factors including age,total prostate-specific antigen(tPSA),diffusion coefficient(D)value,pseudo diffusion coefficient(D*)value,perfusion fraction(F)value,and apparent diffusion coefficient(ADC)as independent variables were compared between the two groups.Binary logistic regression analysis was further used to identify the factors associated with high or low risk of PCa.Receiver operation characteristic(ROC)curves were plotted to evaluate the diagnostic efficacy of PSA,D value,anatomical zones,and the combined model of PSA+D value+anatomical zones in predicting the risk level of PCa.Results The risk level was higher in PZ-PCa group than in TZ-PCa group(P=0.015).Binary logistic regression analysis showed that the tPSA level in the high-risk group of PCa was higher than that in the low-risk group(OR=1.026,95％CI:1.004-1.049,P=0.014),but the Dmean value was lower than that in the low-risk group(OR=0.993,95％CI:0.987-0.999,P=0.034).PCa in the high-risk group was more distributed in the peripheral zone(OR=5.250,95％CI:1.468-18.772,P=0.023).The diagnostic efficacy of the combined model(AUC=0.887,95％CI:0.787-0.987)was higher than that of tPSA,Dmean,or anatomical partitioning alone(P=0.001,0.043,and 0.003,respectively).Conclusion PZ-PCa has a higher risk level than TZ-PCa.Combining bpMRI localization of anatomical zones with PSA and D value provides the highest efficacy in predicting the risk level of PCa,which can potentially support the development of precise and personalized clinical diagnosis and treatment strategies for PCa.