ObjectiveTo investigate the effects of salidroside （SAL） on the impaired bioactivity of endothelial progenitor cells （EPCs） in atherosclerotic （As） mice and the potential mechanisms regarding AMP-activated protein kinase （AMPK）. MethodsAtherosclerosis was induced in 8-week-old male ApoE^-/- mice with high-fat diet. Intragastric administration of SAL was given to one mice group to investigate the effects of SAL on aortic plaque burden， plasma NO level， the migration and angiogenic capabilities of bone marrow-derived EPCs （BM-EPCs）. The proliferation， migration and vasculogenic properties of EPCs isolated from As mice were investigated in vitro. AMPK-sh-RNA or the AMPK inhibitor Compound C was used to investigate the role of AMPK/Akt/eNOS pathway in the regulatory effects of SAL. ResultsCompared with As group， NO level was significantly elevated in SAL group. The sizes of atherosclerotic plaques at the aortic root were reduced with smaller lipid cores in SAL group compared with As group. Moreover， the migration and angiogenesis capacity of EPCs markedly decreased in As mice， while SAL treatment reversed these impairments. Incubation with SAL at concentrations of 20， 40， and 80 μmol/L for 48 hours significantly promoted the proliferation， migration， and angiogenesis of EPCs. AMPK-sh-RNA transfection abrogated the 20 μmol/L SAL improvement in EPC biological activities. Western blot analysis further demonstrated that treatment with Compound C blocked the activation of AMPK/Akt/eNOS signaling pathway induced by SAL. ConclusionSAL upregulates the biological functions of EPCs through activating the AMPK/Akt/eNOS signaling pathway， thereby ameliorating EPC dysfunction during the pathological progression of atherosclerosis.

BACKGROUND/OBJECTIVES: Unhealthy diets are associated with chronic stress. An allostatic load (AL) is a comprehensive physiological index that measures the chronic stress response of the body. As a healthy dietary pattern, the role of Dietary Approaches to Stop Hypertension (DASH) in the development of an AL during pregnancy remains unclear. This study examined the relationship between the DASH dietary pattern and AL during pregnancy. SUBJECTS/METHODS: This study was a prospective study. From January 2024 to November 2024, 134 pregnant women in the second trimester (23–27 weeks) and third trimester (32–36 weeks) of pregnancy who met the inclusion and exclusion criteria in the obstetrics clinic of a tertiary general hospital in Anhui Province were selected for a questionnaire survey, physical examination, and laboratory examination. The DASH dietary pattern was assessed using the DASH scoring tool, and the total AL score was calculated using nine biomarkers that represent the cardiovascular, metabolic, and immune systems. Logistic regression was used to analyze the relationship between the DASH score and AL. RESULTS: One hundred and thirty-four pregnant women were included in this study. At the second and third trimesters, 41.8% and 37% of pregnant women, respectively, were in the high AL group. The binary logistic regression results showed that the DASH score was negatively correlated with the AL in the unadjusted model (odds ratio [OR], 0.878; 95% confidence interval [CI], 0.807–0.957; P = 0.003) and adjusted model (OR, 0.878; 95% CI, 0.792–0.973; P = 0.013) in the second trimester, the DASH score was negatively correlated with the AL in the unadjusted model (OR, 0.832; 95% CI, 0.758–0.913; P < 0.001) and adjusted model (OR, 0.806; 95% CI, 0.716–0.908; P < 0.001) in the third trimester. CONCLUSION The DASH score was negatively correlated with the AL in pregnant women.A low DASH score may increase the risk of a high AL, which may have adverse effects on physical and mental health.

Background: and Purpose Accurately predicting long-term functional outcomes of basilar artery occlusion (BAO) remains challenging. We compared the predictive performance of the baseline, 24-hour, and 72-hour National Institutes of Health Stroke Scale (NIHSS) scores for 90-day BAO functional outcomes using the Acute Basilar Artery Occlusion: Endovascular Thrombectomy versus Standard Medical Treatment (ATTENTION) trial data. We identified the optimal assessment time point, determined treatment-specific NIHSS cutoff values, and explored the role of early neurological function in treatment effects. Methods: This retrospective post hoc analysis included 324 patients with acute BAO with baseline NIHSS scores ≥10 and complete NIHSS assessments at each time point. The primary outcome was a favorable 90-day functional outcome (modified Rankin Scale score, 0–3). Receiver operating characteristic curve analysis was used to assess the predictive ability of NIHSS scores. The optimal 72-hour NIHSS predictive cutoff values were determined for the endovascular treatment (EVT) and best medical management (BMM) subgroups. Results: The 72-hour NIHSS score showed the highest predictive accuracy for the primary outcome (area under the receiver operating characteristic curve [AUC]: 0.954), outperforming the 24-hour (AUC: 0.903) and baseline (AUC: 0.688) scores; its optimal predictive cut-off value was ≤11 in the EVT group (sensitivity: 85.6%, specificity: 92.9%, positive predictive value [PPV]: 91.8%, negative predictive value [NPV]: 87.4%) and ≤9 in the BMM group (sensitivity: 84.6%, specificity: 95.1%, PPV: 84.6%, NPV: 95.1%). Conclusions The 72-hour NIHSS score outperformed the baseline and 24-hour scores in predicting 90-day functional outcomes and mediating the effects of EVT. Treatment-specific 72-hour NIHSS cut-off values may guide early risk stratification and prognostic assessments.

Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

Objective To explore the molecular mechanism of Jianfu mixture in the treatment of erectile dysfunction （ED） by network pharmacology and molecular docking techniques, and validate its core targets and mechanisms through in vitro experiments. Methods The active components and corresponding molecular targets of Jianfu mixture were searched by searching TCMSP and Batman-TCM databases, and the disease targets of ED were searched by using GeneCards database. Find the intersection of drug ingredient target and disease target. The interaction between intersected targets was described and analyzed by String database, and the analysis results were visualized by Cytoscape software to determine the core target and the corresponding active components. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for intersection targets; the core target within the intersection were found through MCODE plug-in on Cytoscape software and molecular docking was performed with the corresponding active ingredients. An endothelial dysfunction model was established by transfecting HUVECs with si-eNOS. Intervene with different concentrations of the Jianfu mixture for the model cells for 24 h. QPCR was used to detect mRNA expression of core targets （MAPK1, MAPK3, JUN, ESR1, MAPK8）; Western blot was used to analyze protein expression （eNOS, JUN, p-JUN, MAPK, p-MAPK） and phosphorylation levels. Results 144 effective active components and 168 active components target-disease targe intersection of Jianfu mixture were obtained. GO analysis revealed 200 5 biological processes, 151 molecular functions, and 63 cellular components. KEGG analysis yielded 181 pathways. 5 core targets including MAPK1, MAPK3, JUN, ESR1 and MAPK8 were screened out. The active components such as β-sitosterol, kaempferol, astapterocarpan had good binding affinity with the core target. In vitro experiments confirmed successful construction of the endothelial dysfunction model （eNOS expression significantly decreased after si-eNOS transfection）. Jianfu mixture dose-dependently inhibited mRNA expression of MAPK1, MAPK3, JUN, ESR1, and MAPK8. Additionally, it reduced phosphorylation levels of JUN and MAPK, indicating inhibition of the JNK/c-Jun and ERK/MAPK signaling pathways to improve endothelial function. Conclusion Jianfu mixture treats ED by suppressing abnormal activation of multi-target signaling pathways （MAPK/JUN/ESR1）, reducing endothelial apoptosis, and promoting NO synthesis. This mechanism aligns with the traditional Chinese medicine principle of “activating blood circulation, resolving stasis, tonifying Qi, and strengthening cardiovascular function.” The study provided molecular-level evidence for the therapeutic efficacy of Jianfu mixture in ED management.

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.

ObjectiveTo observe the therapeutic effect of Erchentang on depression in obese mice and explore the therapeutic mechanism. MethodsC57BL/6J mice were randomized into the control， model， metformin （0.65 g·kg^-1）， and low-， medium-， and high-dose （3.35， 6.7， and 13.4 g·kg^-1， respectively） Erchentang groups. Gavage was initiated simultaneously with modeling and continued for 28 days. The therapeutic effects of Erchentang were evaluated through behavioral tests， liver and brain indices， liver function， lipid indicators， and histopathological changes in the liver and brain tissue. RNA-seq technology was used to conduct transcriptomic analysis of mouse liver tissue， and differentially expressed genes were screened and subjected to Kyoto Encyclopedia of Genes and Genomes （KEGG） analysis. Key genes were further verified by Western blot and Real-time polymerase chain reaction（Real-time PCR）. ResultsBehavioral tests， liver function， lipid indicators， and histopathological changes in the liver and brain tissue all demonstrated that Erchentang had therapeutic effects on depression in obese mice. KEGG pathway enrichment analysis of transcriptomic data indicated that Erchentang treated depression in obese mice mainly through the peroxisome proliferator-activated receptor （PPAR） signaling pathway. Western blot and Real-time PCR results showed that compared with the model group， the low-， medium-， and high-dose Erchentang groups exhibited downregulated expression of fatty acid-binding protein （FABP） 1 （P<0.01） and upregulated expression of acyl-CoA oxidase （ACOX） 1， PPARα， cholesterol 7α-hydroxylase （CYP7A） 1， and phosphoenolpyruvate carboxy kinase （PCK） 1 （P<0.05） in the liver tissue. ConclusionErchentang exerts therapeutic effects on obesity-associated depression by regulating the PPAR signaling pathway and the expression of related genes FABP1， ACOX1， PPARα， CYP7A1， and PCK1.

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.

OBJECTIVE To investigate the mechanism by which resveratrol improves renal injury in rats with uric acid nephropathy （UAN） through inhibition of the protein kinase R-like endoplasmic reticulum kinase （PERK）/eukaryotic translation initiation factor 2α （eIF2α） pathway.METHODS A UAN rat model was established via oral administration of adenine plus potassium oxonate， and the rats were divided into a UAN model group， a positive control group （allopurinol 10 mg/kg）， a resveratrol group （100 mg/kg）， and a resveratrol+tunicamycin group [resveratrol 100 mg/kg+tunicamycin （PERK activator） 1 mg/kg]. A control group （without modeling） was also established， with 6 rats in each group. Rats in each group were administered the corresponding drug solution and/or normal saline via gavage or intraperitoneal injection once daily for 28 consecutive days. The contents of renal function-related parameters （24-hour urinary total protein， and uric acid， creatinine and blood urea nitrogen in serum）， the levels of inflammation-related markers [interleukin-6 （IL-6）， IL-18， tumor necrosis factor-α （TNF-α）， IL-10， and nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3）]， and pathological changes in renal tissue were determined， and the score for tubular damage， the score for inflammatory infiltration， and the degree of mesangial proliferation were assessed. The mRNA expressions of neutrophil gelatinase-associated lipocalin （NGAL） and kidney injury molecule-1 （KIM-1）， as well as the expressions of proteins associated with the PERK/eIF2α pathway， were measured in renal tissue from each group.RESULTS Compared with the control group， rats in the UAN model group exhibited marked tubular damage， inflammatory cell infiltration， and mesangial proliferation. The contents of renal function-related parameters， levels of IL-6， IL-18， TNF-α and NLRP3， scores for tubular damage and inflammatory infiltration， degree of mesangial proliferation， mRNA expressions of NGAL and KIM-1， and phosphorylation levels of PERK and eIF2α， as well as the expressions of activating transcription factor 4 and C/EBP homologous protein， were all significantly increased or upregulated， while IL-10 levels were significantly decreased （ P ＜0.05）. Compared with the UAN model group， the resveratrol group and the positive control group showed significant improvements in all of the aforementioned parameters （ P ＜0.05）. Tunicamycin could significantly reverse the beneficial effects of resveratrol on all quantitative parameters （ P ＜0.05）.CONCLUSIONS Resveratrol can reduce inflammatory responses in renal tissue of UAN rats， and improve tubular damage and renal function. These effects may be related to the inhibition of endoplasmic reticulum stress mediated by the PERK/eIF2α pathway.