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 investigate the impact of RhE antigen-matched transfusion during liver transplantation on early postoperative recovery and complications. Methods: In this retrospective cohort study, ninety-five patients undergoing liver transplantation at Kunming First People's Hospital between January 2022 and July 2025 were enrolled. Patients were divided into two groups: Group 1 (RhE-mismatched transfusion, n=57) and Group 2 (RhE-matched transfusion, n=38). The baseline data, complete blood counts, hepatic and renal function, coagulation parameters, and complication rates between the two groups were compared at postoperative days 1, 3, 5, 7, and 10. Survival analysis was performed using the Kaplan-Meier method. Results: The baseline characteristics were well-balanced and comparable between the two groups (all P>0.05). The early postoperative mortality rate in the mismatched group (31.58%, 18/57) was significantly higher than that in the matched group (10.53%, 4/38) (P=0.017). The incidence of postoperative hepatic encephalopathy was significantly higher in the mismatched group (50.88%, 29/57) than in the matched group (10.53%, 4/38) (P<0.001). The incidence of postoperative haemorrhage in the mismatched group (24.56%, 14/57) was higher than that in the matched group (5.26%, 2/38), with a statistically significant difference (P=0.014). The incidence of perioperative infection in the mismatched group (28.07%, 16/57) was higher than that in the matched group (10.53%, 4/38), with a statistically significant difference (P=0.04). Corresponding odds ratios (OR) and 95% confidence intervals indicated a lower risk of these adverse events in the matched group. On postoperative day 1, the change in activated partial thromboplastin time (-1.6, 20.5) in the mismatched group was greater than in the matched group (-0.2, 5.5). The change in international normalised ratio (-0.56, 1.22) in the mismatched group was greater than in the matched group (-0.18, 0.32), while the change in albumin (-4.0, 4.8) was smaller in the mismatched group than in the matched group (-2.5, 8.8). On postoperative day 5, the change in albumin (-0.41±7.83) in the mismatched group was smaller than in the matched group (2.68±4.53). At postoperative day 7, the change in albumin in the mismatched group (-0.61±7.38) was smaller than that in the matched group (2.51±5.85), while the change in D-dimer in the mismatched group (0.73, 7.4) was greater than that in the matched group (-1.6, 4.3). On postoperative day 10, the mismatched group exhibited significantly higher fibrinogen levels (-1.21, 1.78) than the matched group (-0.49, 0.97), and significantly longer prothrombin times (-11.3, -2.7) than the matched group (-6.2, -0.8) (all P<0.05). The matched group exhibited a mean overall survival (OS) of 32.803 months (95% CI:29.171-36.436 months), significantly exceeding the mismatched group's 28.996 months (95% CI:24.202-33.790 months). The log-rank test yielded statistically significant results (χ =4.307, P=0.038). Conclusion: Implementing RhE blood group-matched transfusion during liver transplantation may help reduce early postoperative mortality and the incidence of major complication rates, promote faster recovery of coagulation and liver function, and thereby improve short-term patient outcomes.

Purpose: Due to the preservation of the entire stomach after endoscopic resection, the occurrence of metachronous gastric cancer (MGC) remains a possibility. In this study, we investigated the incidence and risk factors for MGC in patients with early gastric cancer who underwent endoscopic submucosal dissection (ESD) and successfully eradicatedHelicobacter pylori. Materials and Methods: A retrospective analysis was conducted of 1,191 patients who underwent ESD and successfully eradicated H. pylori at the Affiliated Drum Tower Hospital of Nanjing University. Endoscopic surveillance was performed at 3, 6, and 12 months post-resection, and annually thereafter. MGC was defined as the development of a new cancer at a site other than the primary gastric cancer site, at least 1 year after the initial endoscopic resection. Results: A total of 77 patients were diagnosed with MGC during a median follow-up of 41.5 months. Kaplan-Meier analysis showed a 5-year cumulative incidence of MGC of 9.4% after successful H. pylori eradication. Multivariate analysis of the training set using Cox proportional hazards models identified male sex, severe atrophic gastritis, multiple gastric cancers before H. pylori eradication, and smoking history as independent risk factors for MGC.The nomogram exhibited favorable discrimination, with area under the curves of 0.767 and 0.822 in the training set and 0.724 and 0.745 in the testing set at 3 and 5 years, respectively. Conclusions Patients with gastric cancer who undergo endoscopic resection, even after successful H. pylori eradication, should undergo annual and continuous endoscopic surveillance for MGC.

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.

Objective: To explore the relationship between life skills, psychological resilience and subjective well-being among primary and secondary school students, so as to provide evidence for targeted adolescent mental health education. Methods: From September to November, 2025, a total of 5 831 primary and secondary school students from five cities (Beijing, Guangzhou, Shenzhen, Chongqing, Urumqi) were selected by convenience sampling. The Adolescent Subjective Well being Scale, School based Adolescents Life Skills Scale, and Adolescent Psychological Resilience Scale were used for investigation. Data were analyzed using t-tests, one way ANOVA, and linear regression. Results: The total mean score of students subjective well being was (5.46± 0.91) ; the total mean score of life skills was (3.69±0.64); the total mean score of psychological resilience was (3.66±0.69). Statistically significant differences in subjective well being scores were found among students grouped by gender, grade, school stage, residential city, living with parents or living with siblings ( t/F =4.14, 53.92, 14.53, 49.99, 13.49, 2.18, all P <0.01). Stratified regression analyses were performed by gender (adjusting for school stage, residential city and living arrangement) and by school stage (adjusting for gender, residential city and living arrangement), the regression results indicated that life skills of boys, girls, primary school students and junior high school students( β =0.54, 0.53, 0.50, 0.57) and their psychological resilience ( β = 0.56 , 0.60, 0.56, 0.60) were positively associated with subjective well being (all P <0.01). Conclusions Life skills and psychological resilience may be important protective factors for subjective well being among primary and secondary school students. School based mental health education should emphasize life skills training and resilience building to promote students subjective well being.

Objective: To explore the association between parental subjective well-being and subjective well-being among primary and secondary school students, so as to provide evidence for promoting adolescent mental health from a family perspective. Methods: From September to November 2025, a convenience sampling method was used to recruit 4 612 students in grades 4-8 from 12 primary and secondary schools in five cities—Beijing, Guangzhou, Shenzhen, Chongqing, and Urumqi—along with their parents. Students completed the Adolescent Well being Survey Questionnaire, while parents completed the Parental Well being Survey Questionnaire. Data were analyzed using t-test, one way analysis of variance, multiple linear regression, Logistic regression, and stratified subgroup analyses. Results: The maximum score of the subjective well-being scale for primary and secondary school students was 301, with a total score of (224.07±46.63), representing 74.4% of the full score; the maximum score of the parental subjective well-being scale was 120, with a total score of (92.35±15.10), representing 77.0%. Multiple linear regression analysis showed that parental subjective well-being was positively associated with students overall subjective well-being, positive affect, and satisfaction in each domain (friendship, family, school, environment, freedom, and academics), and negatively associated with negative affect ( β =0.45, 0.08, 0.06, 0.07, 0.05, 0.04, 0.05, 0.04, -0.05, all P <0.01). Logistic regression and stratified analysis showed that parental subjective well-being was positively associated with students high subjective well-being ( OR =1.03), and the association existed in subgroups of different gender, grade and family structure ( OR =1.02-1.04) (all P <0.05). Conclusions Parental subjective well-being is positively associated with students subjective well-being. Future interventions should incorporate an integrated strategy featuring family-school collaboration to promote adolescent well-being.

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 inhibitory effect of Xiaojianzhong Granule(XJZG)on food allergy(FA)and related mecha-nisms in terms of gut microbiota,zonula occluden-1(ZO-1),and Occludin.Methods:A total of 24 specific pathogen-free female BALB/c mice were randomly divided into normal group,model group,prevention group,and treatment group,with 6 mice in each group.The mice in the prevention group were given XJZG by gavage at a standard dose of 5.85 g/kg/day from 3 days before the first challenge till 4 hours before the last challenge;the mice in the treatment group were given XJZG at the double dose for 3 days based on the allergy score;the mice in the other groups were given an equal volume of distilled water by gavage.At the end of the experiment,al-lergy score and anal temperature were measured;flow cytometry was used to measure eosinophils and mast cells in mesenteric lymph nodes(MLNs);toluidine blue staining was performed for mast cells in jejunal tissue;immunohistochemistry was used to measure the expression of ZO-1 and Occludin;16S rRNA sequencing was per-formed to analyze the microbiota in the intestinal content;high-performance liquid chromatography-mass spectrometry was used to measure the content of short-chain fatty acids(SCFAs)in jejunal lavage fluid.Results:Compared with the model group,the prevention group and the treatment group had significant reductions in al-lergy score(P=0.000,P=0.000),anal temperature(P=0.002,P=0.000),the proportion of eosinophils and mast cells in MLNs(P<0.05),and mast cell infiltration in jejunal tissue(P=0.000,P=0.000).Compared with the normal group,the model group had signifi-cant increases in the relative abundances of Erysipelaceae and Turicibacter,while the prevention group and the treatment group had disappearance of Erysipelaceae and Turicibacter and an increase in the relative abundance of Porphyromonadaceae.Compared with the normal group,the model group had a significant reduction in the content of propionate in jejunal lavage fluid(P=0.014),and compared with the model group,the prevention group had a significant increase in the content of propionate in jejunal lavage fluid(P=0.024),as well as a significant increase in the treatment group(P=0.008).In the model group,the expression of ZO-1 was downregulated(P=0.010),and the expression of Occludin was significantly downregulated(P=0.002),while the expression of ZO-1 and Occludin re-turned to normal levels in the prevention group and the treatment group(P=0.001,P=0.013;P=0.025,P=0.015).Conclusion:XJZG can change the composition and abundance of gut microbiota,increase the concentration of SCFAs,upregulate the expression of ZO-1 and Occludin,promote the repair of intestinal barrier,and inhibit food allergy.

Objective:To explore the application effect of early enteral nutrition combined with optimized blood glucose management in patients with stress hyperglycemia after cardiac surgery.Methods:A study was conducted to construct an early enteral nutrition and blood glucose optimization management plan for patients with stress hyperglycemia after cardiac surgery.A prospective analysis was conducted from May 2022 to May 2023 in the Cardiothoracic Surgery Intensive Care Unit(CICU)of a tertiary hospital in Shanghai.200 patients with stress hyperglycemia after cardiac surgery were divided into control group(n=100)and observation group(n=100)by random number table method.The control group received routine enteral nutrition and blood glucose management plan,while the observation group received early enteral nutrition combined with blood glucose optimization management plan.Nutrition related outcome indicators,blood glucose management quality indicators,and patient prognosis indicators were compared between two groups of patients.Results:After implementing the early enteral nutrition combined with optimized blood glucose management plan,the nutritional indicators(serum prealbumin,hemoglobin,and 7-day calorie compliance rate)of the observation group were higher than those of the control group,and the differences were statistically significant(P<0.05);The maximum blood glucose fluctuation amplitude,insulin use time,and incidence of hypoglycemia in the observation group were significantly lower than those in the control group(P<0.05),and the time within the glucose target range was significantly higher than that in the control group,with statistical significance(P<0.05);The mechanical ventilation time,ICU stay time,and total hospital stay in the observation group were significantly lower than those in the control group,and the differences were statistically significant(P<0.05).Conclusions:Early enteral nutrition combined with optimized blood glucose management can effectively improve the nutritional status of patients with stress hyperglycemia after cardiac surgery,reduce blood glucose fluctuations,improve blood glucose management quality,and promote patient outcomes and prognosis.

Objective:To evaluate the effect of dietary adjustment guided by body composition analysis on blood glucose,,blood lipids,weight gain and pregnancy outcome in patients with gestational diabetes mellitus(GDM).Methods:A total of 122 GDM patients who had regular prenatal check-ups and gave birth at Wenzhou People's Hospital from May 2021 to October 2023 were selected as the research subjects and were randomly divided into Group A and Group B,with 61 cases in each group.Group A was given routine nutritional intervention with GDM,and Group B was adjusted their dietary structure based on body composition analysis.Another 50 normal pregnant women were selected as the control group.Three groups were compared for blood glucose,lipids,and body mass growth and pregnancy outcomes.Results:After intervention,the levels of FPG and 2hPG in the three groups were significantly decreased,while the levels of TC,TG,and LDL-C were significantly increased;The HbA1c levels in Group A and Group B were decreased significantly,while the HDL-C levels in the control group were significantly increased(P<0.05).After intervention,the levels of FPG,2hPG,HbA1c,TC,TG,and LDL-C in Group A were significantly higher than those in Group B and the control group(P<0.05).After intervention,the levels of FPG,TC,and LDL-C in group B were significantly higher than those in the control group(P<0.05).The predelivery body mass and increased body mass during pregnancy in Group A and Group B were significantly higher than those in the control group,while Group B was significantly lower than Group A(P<0.05).There was no significant difference in the compliance rate of body mass increase during pregnancy between the control group and group B(P>0.05),but both groups were significantly higher than group A(P<0.05).There was no significant difference in the cesarean section rate,pregnancy complications,and overall incidence of adverse perinatal outcomes between the control group and group B(P>0.05),but they were significantly lower than group A(P<0.05).Conclusion:Dietary structure adjustment guided body composition analysis can help to control blood glucose levels in GDM patients,maintain reasonable weight gain during pregnancy,reduce cesarean section rates,and reduce the risk of pregnancy complications and adverse perinatal outcomes.