Abstract Adverse childhood experiences (ACEs) exposure is a pressing and severe global public health issue. Children and adolescents exposed to multiple ACEs are highly susceptible to toxic stress and impaired physiological functioning, which significantly jeopardize their physical and mental health. Effective prevention and intervention strategies can reduce the prevalence of ACEs and mitigate their severe impacts, thereby minimizing the long term detrimental consequences on future outcomes. The review provides a comprehensive review of intervention strategies across four dimensions: individual, family, school, and public services/policy, so as to establish a theoretical foundation for implementing effective interventions for children and adolescents exposed to adverse childhood experiences.

Objective: To understand the impact of childhood trauma on psychological distress among upper grade elemetary school students, and to explore the mediating role of leisure activities in the relationship, so as to provide a basis for developing mental health intervention strategies. Methods: From August to November 2024, a combination of convenience sampling and stratified cluster random sampling was employed to recruit 1 373 fourth to sixth grade students from four primary schools in Harbin. The Childhood Trauma Questionnaire(CTQ), a self designed leisure activity scale (including active and passive leisure activities), and the Kessler Psychological Distress Scale (K10) were used to assess childhood trauma experiences, leisure activities, and levels of psychological distress. Spearman correlation analysis and linear regression analysis were conducted to explore the relationships among childhood trauma, leisure types, leisure time, and psychological distress. Based on the mediation analysis framework proposed by Hayes (Model 4), the mediating role of leisure types in the relationship between childhood trauma and psychological distress was examined. Results: Totally 19.1% of the upper elemetary school students exhibited psychological distress, while 30.2% had experienced childhood trauma. During school days, 64.6% of the students were reported of having leisure time concentrated between 1 and 5 hours per day, whereas 67.4% reported leisure time exceeding 5 hours per day on weekends. After controlling for potential demographic confounders such as gender, grade, ethnicity, household registration, being an only child, parents educational level, co residence, and whether parents are first time married,linear regression analysis showed that childhood trauma experience had positive predictive effect on psychological distress in upper primary school students( β =0.20, P <0.01). Leisure time showed no statistically significant association with psychological distress, both on school days ( β =-0.58 to -0.56) and weekends ( β =0.26- 0.98 )(all P >0.05). Active leisure activities were negatively associated with psychological distress ( β =-0.20), while passive leisure activities were positively associated with psychological distress ( β =0.29)(both P <0.01). Leisure type partially mediated the relationship between childhood trauma and psychological distress, accounting for 11.7% of the indirect effect. Conclusion Childhood trauma experiences positively predict psychological distress in upper elementary school students, and affect psychological distress through active leisure and passive leisure.

ObjectiveTo investigate the effect of honokiol on proliferation， apoptosis， migration， and invasion of gastric cancer cells and its underlying mechanistic. MethodsHuman gastric cancer cell lines HGC-27 and AGS were treated with Honokiol at concentrations of 0， 15， and 25 μmol/L. CCK-8 assays were conducted to determine the half maximal inhibitory concentration （IC₅₀） for both cell lines. Cell viability， proliferation， migration， and invasion capabilities were assessed using CCK-8， colony formation， wound healing， Transwell migration and Transwell invasion assays. Apoptosis rates were measured via flow cytometry. Western blot analysis examined proteins related to proliferation， apoptosis， migration， invasion， and the endoplasmic reticulum stress pathway ATF4-CHOP-TRIB3. ResultsCompared with the control group， treatment with 15 and 25 μmol/L Honokiol significantly reduced the proliferation， colony formation， migration， and invasion capabilities of the two gastric cancer cell lines， while significantly increasing the apoptosis rate （P<0.05）. Additionally， compared to the control group， the protein expression levels of neural cadherin（N-cadherin）， Vimentin， proliferating cell nuclear antigen（PCNA）， and B-cell lymphoma/leukemia-2 protein（Bcl-2）decreased in the two gastric cancer cell lines after treatment with 15 and 25 μmol/L Honokiol， while the protein expression levels of epithelial cadherin（E-cadherin）， Bcl-2-associated X protein（Bax）， activating transcription factor 4（ATF4）， endoplasmic reticulum stress-related protein（CHOP）， and tribbles homolog 3（TRIB3）increased（P<0.05）. ConclusionHonokiol promotes apoptosis and inhibits proliferation， migration， and invasion of gastric cancer HGC-27 and AGS cells by regulating the ERS signaling pathway ATF4/CHOP/TRIB3.

Abstract Adolescents are a high risk group for non suicidal self injury (NSSI), and the severity of the issue has been increasing. However, stigma originating from self perceptions, the public, and healthcare professionals poses significant barriers to help seeking and recovery among adolescents with NSSI. The paper systematically reviews the main intervention strategies targeting NSSI related stigma in adolescents, along with their effectiveness, from three perspectives: educational approaches, contact based interventions, and public advocacy against stigma, aiming to inform the development of a scientific, effective, and multi stakeholder collaborative integrated intervention framework to provide a basis for reducing and ultimately eliminating stigma associated with adolescent NSSI.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

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.

AIM To improve the quality standard for Dahuang Sodium Bicarbonate Tablets.METHODS TLC was adopted in the qualitative identification of rhein,emodin,chrysophanol,aloe-emodin,physcion and rhapontin,after which UPLC fingerprints were established,content determination of aloe emodin-8-O-β-D-glucoside,rhein-8-O-β-D-glucoside,emodin-8-O-β-D-glucoside,chrysophanol-8-O-β-D-glucoside,physcion-8-O-β-D-glucoside,aloe-emodin,rhein,emodin,chrysophanol,physcion were performed.RESULTS The clear TLC plots demonstrated good specificity.There were 20 common peaks in the fingerprints for 8 batches of samples with the similarities of more than 0.535.Ten constituents showed good linear relationships within their own ranges(r≥0.997 0),whose average recoveries were 98.7％-104.1％with the RSDs of 0.59％-2.13％,whose content ranges were 0.274 1-2.005 9,0.547 8-5.192 2,1.209 2-3.936 3,1.039 3-3.330 2,0.307 0-1.248 1,0.118 2-0.674 7,0.115 1-0.970 3,0.224 5-1.789 3,0.881 4-4.882 6,0.801 4-5.099 1 mg/g,respectively.CONCLUSION This simple and stable method can effectively control the quality of Rhei Radix et Rhizoma in Dahuang Sodium Bicarbonate Tablets.

Endometriosis(EMs)is a common estrogen-depend-ent clinical disease with the pathological characteristics of malig-nant tumors,which has great impact on women's physical and mental health.In recent years,experimental exploration has re-vealed that ectopic foci are in a hypoxic environment outside the uterine cavity,and mitochondria,as the"functional factories"of the cells,play an important role in the process of planting and in-vasion,and the mitochondrial quality control system,which in-cludes mitochondrial oxidative stress,kinetics,autophagy,bio-genesis and calcium homeostasis,is a key mechanism for the e-quilibrium of the mitochondrial function.The mitochondrial quality control system,including mitochondrial oxidative stress kinetics,autophagy,biogenesis and calcium homeostasis,is a key mechanism for mitochondrial functional balance.Therefore,to clarify the role of the mitochondrial quality control system in the development of EMs with the help of rational and rigorous experi-mental and clinical studies can not only help to clarify the patho-genesis of the disease,but also explore the key targets in the prevention and treatment of the disease.Therefore,this article summarizes the research progress of mitochondrial quality control system in endometriosis,with a view to providing reference and theoretical basis for the etiology,pathogenesis and prevention strategies of EMs.

Objective:This study aimed to investigate the value of myocardial contrast echocardiography(MCE)in the prognostic assessment of patients undergoing revascularization for acute anterior ST-segment elevation myocar-dial infarction(STEMI).Methods:A total of 234 patients with acute anterior STEMI who underwent percutaneous coronary intervention(PCI)and completed MCE admitted in Dongguan People's Hospital between July 1st 2019 and October 1st 2021 were included.According to presence of major adverse cardiovascular events(MACE)during 6 months,patients were divided into no MACE group(n=188)and MACE group(n=46).General data and MCE indexes were compared between the two groups.Multivariate Logistic regression analysis was employed to analyze influencing factors for MACE in patients with acute anterior STEMI after PCI.Receiver operating characteristic(ROC)curve was applied to analyze predictive value of MCE indexes for MACE in patients with acute anterior STE-MI after PCI.Results:Compared with patients in no MACE group,those in MACE group had significant higher proportion of previous myocardial infarction,D-dimer,C reactive protein,B-type natriuretic peptide,creatine kinase isoenzyme MB(CK-MB),incidence of T-wave inversion and ST-segment resolution amplitude(P＜0.05 or＜0.01).MCE showed that compared with those in no MACE group,those in MACE group had significant lower left ventricular ejection fraction(LVEF)[(52.54±7.66)％ vs.(55.98±10.04)％],and significant higher wall motion score index(WMSI)[(1.22±0.13)vs.(1.17±0.15)]and contrast score index(CSI)[(1.54±0.32)％vs.(1.16±0.21)％](P＜0.05 or＜0.01).Multivariate Logistic regression analysis revealed that previous myocar-dial infarction,C-reactive protein,CSI and T-wave inversion were independent risk factors for MACE in patients with acute anterior STEMI after PCI(OR=13.790～6601.747,P＜0.05 or＜0.01).ROC curve indicated that CSI had good predictive value for MACE in patients with acute anterior STEMI after PCI,the area under curve(AUC)was 0.874(95％CI 0.825～0.914)and optimal cutoff point was 1.33％.Conclusion:Myocardial contrast echocardiography plays an important role in prognostic assessment of patients undergoing revascularization for acute anterior STEMI.