ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

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.

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.

Medical device related infections caused by bacteria are common complications in clinical practice,and preventing bacterial colonization on the surface of medical materials is one of the important challenges in the medical field.Therefore,there is an urgent need to construct medical coatings that combine antibacterial properties and biocompatibility.In this study,a γ-polyglutamic acid(γ-PGA)complex with long-chain alkyl quaternary ammonium salts formed by electrostatic and hydrophobic interactions was prepared,which was insoluble in water but soluble in organic solvents(e.g.,ethanol),and was capable of constructing antimicrobial coatings on the surfaces of medical materials in a simple and efficient manner.The bactericidal effect of the coating was verified using viable bacteria counting experiments,and the results showed that the bactericidal rate of the coated thermoplastic polyurethane(TPU)membrane against Staphylococcus aureus was greater than 99.9%compared with that of the uncoated TPU membrane.In addition,a cytotoxicity assay was performed using the L929 fibroblast and cell proliferation detection kit(CCK-8),which showed that the survival rate of L929 fibroblasts on coated TPU was greater than 90%.Meanwhile,the hemolysis rate of coated erythrocytes was tested using fresh rabbit red blood cells(RBCs),and the hemolysis rate on the coated TPU surface was 1.5%.The above results indicated that the coating had good biocompatibility.The preparation method of medical antibacterial coating reported in this study provided a new idea for preventing bacterial infections related to implantable/interventional medical devices.

The 2024 National Education Work Conference pointed out that at the current juncture of the critical period for achieving the goals and tasks of the 14th Five-Year Plan,the implementation of the Education Powerhouse Construction Plan Outline should be taken as the main line of work,and building first-class disciplines is an crucial task for a higher education powerhouse.In 2022,forensic medicine was officially listed as a first-level discipline under the medical category,presenting an un-precedented historical opportunity for the development of forensic medicine.The forensic medicine dis-cipline of Southern Medical University comprehensively improves the quality of talent cultivation and facilitates the construction of first-class disciplines as its main direction.It aims to initiate and imple-ment a high-level faculty team building plan featuring"combining recruitment and cultivation,inter-disciplinary integration";make vigorous efforts to establish a first-level doctoral program,refine advan-tageous second-level disciplines and research directions;and establish an innovative research platform from a high starting point with deep integration.The discipline adheres to moral cultivation and the Five Domains of Education simultaneous development,to build a high-quality talent joint training model.Guided by the construction of the national legal system and industry needs,the discipline will enhance social service capabilities.The forensic medicine construction in our university will continue to contribute to the rule of law in China and educational power.

Objective To establish a chromatography-tandem mass spectrometry method for detecting chlorfenapyr and its metabolite tralopyril in blood,and to investigate the toxicokinetics in rats.Methods Chlorfenapyr(8 mg/kg)was administered orally to rats,and blood samples were collected from rats'canthus vein at 5 min,15 min,30 min,1 h,3 h,6 h,12 h,24 h and 48 h after administration.The blood samples were extracted using 100 μL of 5%formic acid solution and 400 μL of acetonitrile.Chlorfena-pyr was qualitatively and quantitatively detected by triple quadrupole gas chromatography-tandem mass spectrometry(GC-MS/MS)and tralopyril was detected by triple quadrupole liquid chromatography-tandem mass spectrometry(LC-MS/MS).The DAS 3.0 software was used to fit the toxicokinetic equa-tions and calculate the toxicokinetic parameters.Results Chlorfenapyr was detectable from 5 min to 24 h with a peak time of 1 h.Tralopyril was detectable from 15 min to 48 h with a peak time of 3 h.The toxicokinetic process of chlorfenapyr in rat blood conformed to a first-order absorption one-compartment open model,with the toxicokinetic equation described as C=e-0.265t-e-0.175t.Tralopyril con-formed to the first-order absorption three-compartment model,and the toxicokinetic equation was C=47 361.069e-2.209t-35 404.962e-1.486t+11 956.363e-0.512t.In the equations,C stands for the concentration of the target substance in the blood,e is the natural constant(≈2.718 28),and t stands for time.Conclu-sion This study optimized the detection method for chlorfenapyr and its metabolite tralopyril in blood.The toxicokinetic equations and parameters of chlorfenapyr and tralopyril can provide a reference for the estimation of oral intake time of chlorfenapyr.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

Objective:To construct a decision tree model to predict the risk of breast cancer in patients with pathological nipple discharge.Methods:A total of 157 patients with pathological nipple discharge,who were diagnosed and treated at Weifang Municipal Hospital of Traditional Chinese Medicine from January 2019 to April 2024 and met the inclusion criteria,were selected.A risk prediction model for concurrent breast cancer in patients with pathological nipple discharge was developed using Logistic regression analysis.A decision tree was then constructed,and the predictive performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC).Re-sults:The incidence of concurrent breast cancer among patients with pathological nipple discharge was 24.2%.Accord-ing to the results of binary Logistic regression analysis,elevated CEA and CA 153 levels in nipple discharge,as well as bloody discharge,emerged as independent risk factors for the development of breast cancer in such patients(P<0.05).Based on these findings,a decision tree model was constructed to predict the risk of concurrent breast cancer in patients with pathological nipple discharge.The validation results showed that the Logistic regression model had an AUC value of 0.800,while the decision tree model achieved an AUC value of 0.889.Conclusions:The decision tree model,built upon the identified influencing factors,exhibits strong predictive power for the risk of developing concurrent breast can-cer in patients with pathological nipple discharge,thus facilitating more precise preoperative diagnoses by clinicians for these patients.

AIM To compare total sugar,total protein,total phenol,total flavonoid,calycosin-7-O-β-D-glucoside,liquiritin,lobetyolin,quercetin,isoferulic acid,hesperidin,glycyrrhizic acid contents and their antioxidant activities,hypoglycemic activities of big honey pill,small honey pill,water pill,concentrated pill,granule,mixture and decoction of Buzhong Yiqi Recipe.METHODS Anthraquinone-sulfuric acid method,Coomassie brilliant blue method,Folin-phenol colorimetry method,sodium nitrite-aluminum nitrate method and HPLC were adopted in the content determination of total sugar,total protein,total phenol,total flavonoid and seven constituents,respectively,after which the scavenging capacities,reducing powers on DPPH·free radical,ABTS+free radical,hydroxyl free radical,and inhibition capacity on α-glucosidase activity were detected.Subsequently,correlation analysis was performed.RESULTS Total sugar,total protein,total phenol and total flavonoid contents demonstrated significant differences among different dosage forms(P＜0.05,P＜0.01).Calycosin-7-O-β-D-glucoside,glycyrrhizin,codonoside and quercetin displayed the highest contents in the decoction,while those of isoferulic acid,hesperidin and glycyrrhizin were observable in the mixture.The water pill exhibited the strongest antioxidant activity,while those of the concentrated pill and mixture were weak;the big honey pill exhibited the strongest hypoglycemic activity,while that of the decoction was the weakest.Total protein,total phenol,total flavonoid and liquiritin contents displayed significant positive correlations between antioxidant activity(P＜0.05,P＜0.01),while hesperidin content displayed significant negative correlation between the latter(P＜0.05);total protein,calycosin-7-O-β-D-glucoside,codonoside and quercetin contents displayed significant negative correlations between hypoglycemic activity(P＜0.05,P＜0.01).CONCLUSION Active constituent contents and their biological activities of Buzhong Yiqi Recipe with different dosage forms exist differences,total sugar,total protein,total flavonoids,calycosin-7-O-β-D-glucoside,licorice glycoside,hesperidin,codonoside and quercetin can be taken as quality control indices for this prescription.