Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

ObjectiveTo observe the clinical efficacy of electroacupuncture in treatment of simple obesity patients and its effects on the metabolism-inflammation network. MethodsSeventy-two patients with simple obesity were randomly divided into a electroacupuncture group and a sham electroacupuncture group， with 36 patients in each group. In the electroacupuncture group， conventional acupuncture was applied at Zhongwan （CV 12）， bilateral Tianshu （ST 25）， bilateral Daheng （SP15）， Qihai （CV 6）， Guanyuan （CV 4）， bilateral Zusanli （ST 36） and Fenglong （ST 40）. The sham electroacupuncture group used placebo needle at same acupoints as the electroacupuncture group. Both groups were given sparse-dense waves with a frequency of 2/10 Hz， 30 minutes per session， 3 times a week， for a total of 8 weeks of treatment. The changes of human body parameters including body weight， body mass index （BMI）， waist circumference， and body fat percentage （BFP）， metabolic factors including serum fasting blood glucose （FBG）， triglyceride （TG）， total cholesterol （TC）， high density lipoprotein （HDL）， low density lipoprotein （LDL）， leptin， and ghrelin， and inflammatory factors including interleukin-6 （IL-6） and tumor necrosis factor α （TNF-α） were compared before and after treatment. Correlations between changes in leptin， ghrelin and inflammatory factors were further analyzed. ResultsWaist circumference was significantly reduced after treatment in both groups compared with baseline （P＜0.05）， and the post-treatment waist circumference was lower in the electroacupuncture group than in the sham electroacupuncture group （P＜0.05）； body weight， BMI and BFP decreased significantly after treatment compared to pretreatment levels in the electroacupuncture group （P＜0.05）. Serum leptin， IL-6 and TNF-α declined significantly after treatment in both groups versus baseline （P＜0.05）； the electroacupuncture group had lower post-treatment levels of leptin， IL-6 and TNF-α as well as larger pre-post changes than the sham group （P＜0.05）； ghrelin increased markedly after treatment in both groups （P＜0.05）， with higher post-treatment ghrelin and greater pre-post variation observed in the electroacupuncture group （P＜0.05）. In the electroacupuncture group， changes in ghrelin were negatively correlated with IL-6 and positively correlated with TNF-α （P＜0.01）； in the sham electroacupuncture group， ghrelin changes were negatively correlated with both IL-6 and TNF-α， whereas leptin changes were positively correlated with the two inflammatory markers （P＜0.01）. ConclusionElectroacupuncture can effectively improve clinical manifestations， as well as regulate serum metabolic and pro-inflammatory factors in patients with simple obesity， and discrepant correlation patterns of metabolism-inflammation are observed between the two groups.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

To address the challenges of fragmented data, inconsistent standards, and delayed feedback in the full-cycle competency development process at teaching hospitals, this paper proposes the LEADS data governance system for Precision Medical Education (LEADS-PME), drawing on the practical experience of building the integrated medical education management platform at Zhongshan Hospital, Fudan University. The LEADS-PME framework comprises five interconnected core components: Longitudinal foundation, Evidence-chain generation, Analytics and profiling, Decision-support and prediction, and Simulation and digital-twin governance. Currently, Zhongshan Hospital has established a full-cycle educational data infrastructure and achieved multi-system data integration and competency-based profiling and evaluation, while the prediction and simulation modules are still in the prototype exploration phase. A mixed-methods evaluation will be subsequently conducted from the perspectives of data quality, management effectiveness, and predictive intervention. As a locally adapted framework, LEADS-PME can transform operational data derived from clinical practice, teaching, assessment, and management processes into traceable, interpretable, and reusable evidence of competency development. It is expected to facilitate the transformation of medical education management from a outcome-oriented approach to process monitoring, evidence-driven decision-making, and precision governance, thereby providing both a theoretical framework and practical reference for precision medical education in Chinese teaching hospitals.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Alcoholic liver disease(ALD), a major cause of chronic liver disease worldwide, poses a serious threat to human health. Despite the availability of various drugs for treating ALD, their efficacy is often uncertain, necessitating the search for new therapeutic approaches. Traditional Chinese medicine polysaccharides have garnered increasing attention in recent years due to their versatility, high efficiency, and low side effects, and they have demonstrated significant potential in preventing and treating ALD. Emerging studies have suggested that these polysaccharides exert their therapeutic effects through multiple mechanisms, including the inhibition of oxidative stress and the regulation of lipid metabolism, gut microbiota, and programmed cell death. This review summarizes the recent research progress in the pharmacological effects and regulatory mechanisms of traditional Chinese medicine polysaccharides in treating ALD, aiming to provide a scientific basis and theoretical support for their application in the prevention and treatment of ALD.
Humans ; Liver Diseases, Alcoholic/metabolism* ; Polysaccharides/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Oxidative Stress/drug effects* ; Medicine, Chinese Traditional ; Gastrointestinal Microbiome/drug effects* ; Lipid Metabolism/drug effects*

This study aims to delve into the influences and underlying mechanisms of Banxia Xiexin Decoction(BXD) on the proliferation, apoptosis, invasion, and migration of colon cancer cells. Firstly, the components of BXD in blood were identified by UPLC-MS/MS, and subsequently the content of these components were determined by HPLC. Then, different concentrations of BXD were used to treat both the normal intestinal epithelial cells(NCM460) and the colon cancer cells(HT29 and HCT116). The cell viability and apoptosis were examined by the cell counting kit-8(CCK-8) and flow cytometry, respectively. Western blot was employed to determine the expression of the apoptosis regulators B-cell lymphoma-2(Bcl-2) and Bcl-2-associated X(Bax). The cell wound healing assay and Transwell assay were employed to measure the cell migration and invasion, respectively. Additionally, Western blot was employed to determine the expression levels of epithelial-mesenchymal transition(EMT)-associated proteins, including epithelial cadherin(E-cadherin), neural cadherin(N-cadherin), and vimentin. The protein and mRNA levels of the factors in the poly(ADP-ribose) glycohydrolase(PARG)/poly(ADP-ribose) polymerase 1(PARP1)/nuclear factor kappa-B p65(NF-κB p65) signaling pathway were determined by Western blot and RT-qPCR, respectively. The results demonstrated that following BXD intervention, the proliferation of HT29 and HCT116 cells was significantly reduced. Furthermore, BXD promoted the apoptosis, enhanced the expression of Bcl-2, and suppressed the expression of Bax in colon cancer cells. At the same time, BXD suppressed the cell migration and invasion and augmented the expression of E-cadherin while diminishing the expression of N-cadherin and vimentin. In addition, BXD down-regulated the protein and mRNA levels of PARG, PARP1, and NF-κB p65. In conclusion, BXD may inhibit the malignant phenotypes of colon cancer cells by mediating the PARG/PARP1/NF-κB signaling pathway.
Colonic Neoplasms/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Phenotype ; Signal Transduction/drug effects* ; Cell Proliferation/drug effects* ; Apoptosis ; Cell Movement/drug effects* ; Neoplasm Invasiveness ; HCT116 Cells ; Proto-Oncogene Proteins c-bcl-2/biosynthesis* ; Humans ; Poly (ADP-Ribose) Polymerase-1 ; Glycoside Hydrolases ; bcl-2-Associated X Protein ; NF-kappa B p50 Subunit

The Baihe Dihuang Decoction(BDD) is a representative traditional Chinese medicine formula that has been used to treat depression. This study employed metabolomics and network pharmacology to investigate the mechanism of BDD in the treatment of depression. Fifty male Sprague-Dawley(SD) rats were randomly assigned to the normal control group, model group, fluoxetine group, and high-and low-dose BDD groups. A rat model of depression was established through chronic unpredictable mild stress(CUMS), and the behavioral changes were detected by forced swimming test and open field test. Metabolomics technology was used to analyze the metabolic profiles of serum and hippocampal tissue to screen differential metabolites and related metabolic pathways. Additionally, network pharmacology and molecular docking techniques were used to investigate the key targets and core active ingredients of BDD in improving metabolic abnormalities of depression. A "component-target-metabolite-pathway" regulatory network was constructed. BDD could significantly improve depressive-like behavior in CUMS rats and regulate 12 differential metabolites in serum and 27 differential metabolites in the hippocampus, involving tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, alanine, aspartate, and glutamate metabolism, tyrosine metabolism, and purine metabolism. Verbascoside, isorbascoside, and regaloside B were the key active ingredients for improving metabolic abnormalities in depression. Epidermal growth factor receptor(EGFR), protooncogene tyrosine-protein kinase(SRC), glycogen synthase kinase 3β(GSK3β), and androgen receptor(AR) were the key core targets for improving metabolic abnormalities of depression. This study offered a preliminary insight into the mechanism of BDD in alleviating metabolic abnormalities of depression through network regulation, providing valuable guidance for its clinical use and subsequent research.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Depression/genetics* ; Antidepressive Agents/chemistry* ; Network Pharmacology ; Hippocampus/drug effects* ; Humans ; Molecular Docking Simulation ; Behavior, Animal/drug effects* ; Disease Models, Animal

The chemical constituents from leaves of Cyclocarya paliurus were isolated and purified by chromatography on silica gel, C_(18) reverse-phase silica gel, and Sephadex LH-20 gel, as well as semi-preparative high-performance liquid chromatography. Six compounds were identified by UV, IR, NMR, MS, calculated ECD, and comparison with literature data as cyclopaloside D(1), boscialin(2),(5R,6S)-6-hydroxy-6-[(E)-3-hydroxybut-1-enyl]-1,1,5-trimethylcyclohexanone(3), 3S,5R-dihydroxy-6R,7-megastigmadien-9-one(4), 3S,5R-dihydroxy-6S,7-megastigmadien-9-one(5), and gingerglycolipid A(6), respectively. Among them, compound 1 was identified as a new tetralone glycoside, and compounds 2-6 were isolated from leaves of C. paliurus for the first time. Furthermore, compound 1 exhibited strong antioxidant activity, with the IC_(50) of(454.20±31.81)μmol·L~(-1) and(881.82±42.31)μmol·L~(-1) in scavenging DPPH and ABTS free radicals, respectively.
Plant Leaves/chemistry* ; Glycosides/isolation & purification* ; Juglandaceae/chemistry* ; Tetralones/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification*

This study explores the effect and mechanism of Banxia Xiexin Decoction(BXD) in inhibiting colon cancer progression by reshaping tryptophan metabolism. Balb/c mice were assigned into control, model, low-dose BXD(BXD-L), and high-dose BXD(BXD-H) groups. Except the control group, the other groups were subcutaneously injected with CT26-Luc cells for the modeling of colon cancer, which was followed by the intervention with BXD. Small animal live imaging was employed to monitor tumor growth, and the tumor volume and weight were measured. Hematoxylin-eosin(HE) staining was used to observe the pathological changes in mouse tumors. Immunohistochemistry was used to detect Ki67 expression in tumors. Immunofluorescence and flow cytometry were used to detect the infiltration and number changes of CD3~+/CD8~+ T cells in the tumor tissue. Enzyme-linked immunosorbent assay(ELISA) was employed to measure the levels of interferon-gamma(IFN-γ) and interleukin-2(IL-2) in tumors. Targeted metabolomics was employed to measure the level of tryptophan(Trp) in the serum, and the Trp content in the tumor tissue was measured. Western blot and RT-qPCR were employed to determine the protein and mRNA levels, respectively, of indoleamine 2,3-dioxygenase 1(IDO1), MYC proto-oncogene, and solute carrier family 7 member 5(SLC7A5) in the tumor tissue. Additionally, a co-culture model with CT26 cells and CD8~+ T cells was established in vitro and treated with the BXD-containing serum. The cell counting kit-8(CCK-8) assay was used to examine the viability of CT26 cells. The content of Trp in CT26 cells and CD8~+ T cells, as well as the secretion of IFN-γ and IL-2 by CD8~+ T cells, was measured. RT-qPCR was used to determine the mRNA levels of MYC and SLC7A5 in CT26 cells. The results showed that BXD significantly inhibited the tumor growth, reduced the tumor weight, and decreased the tumor volume in the model mice. In addition, the model mice showed sparse arrangement of tumor cells, varying degrees of patchy necrosis, and downregulated expression of Ki67 in the tumor tissue. BXD elevated the levels of IFN-γ and IL-2 in the tumor tissue, while upregulating the ratio of CD3~+/CD8~+ T cells and lowering the levels of Trp, IDO1, MYC, and SLC7A5. The co-culture experiment showed that BXD-containing serum reduced Trp uptake by CT26 cells, increased Trp content in CD8~+T cells, enhanced IL-2 and IFN-γ secretion of CD8~+T cells, and down-regulated the mRNA levels of MYC and SLC7A5 in CT26 cells. In summary, BXD can inhibit the MYC/SLC7A5 pathway to reshape Trp metabolism and adjust Trp uptake by CD8~+ T cells to enhance the cytotoxicity, thereby inhibiting the development of colon cancer.
Animals ; Tryptophan/metabolism* ; Colonic Neoplasms/pathology* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Mice, Inbred BALB C ; Humans ; Cell Line, Tumor ; Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism* ; Female ; Disease Progression ; Cell Proliferation/drug effects* ; Proto-Oncogene Mas ; Male