ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

Endoscopic transforaminal lumbar interbody fusion (TLIF) offers substantial advantages in the management of degenerative spinal diseases, including accelerated postoperative recovery. However, its technical complexity and steep learning curve pose risks for nerve root injury. Optimizing nerve root protection in full-endoscopic facet-sparing TLIF (FE fs-TLIF) and full-endoscopic facet-resecting TLIF (FE fr-TLIF) is essential for enhancing surgical safety. This study aimed to improve the nerve root protection in FE fs-TLIF and FE fr-TLIF by optimizing cage glider selection and insertion techniques based on the specific cage shape—banana-shaped or bullet-shaped. The goal was to ensure safe cage positioning and mitigate nerve root injury during discectomy, endplate preparation, and cage insertion. These strategies were validated through cadaveric simulations and clinical implementation. In FE fr-TLIF utilizing bullet-shaped (straight) cages, one-tip and two-tip cage gliders effectively protected the traversing nerve root by facilitating medial cage entry, thereby minimizing irritation of the exiting nerve root. Conversely, in FE fr-TLIF with banana-shaped cages, the lateral tilt of the cage holder during implantation required the use of a two-tip cage glider to protect the traversing and exiting nerve roots, thereby mitigating the potential risk of nerve irritation. In FE fs-TLIF, a one-tip cage glider is preferred for safeguarding the exiting nerve root, while the traversing root is inherently protected by the medial wall of the facet joint. The use of a two-tip cage glider in FE fs-TLIF can cause injury to the nerve root during glider insertion. In addition to the selection of cage gliders, improper cage insertion steps can also contribute to postoperative neurapraxia. The appropriate selection of cage gliders with corresponding insertion techniques is critical for nerve root protection in endoscopic TLIF. Tailoring these choices to the specific approach (FE fs-TLIF or FE fr-TLIF) and cage type (banana or bullet) enhances surgical safety and clinical outcomes.

Objective To analyze the factors influencing occupational health literacy (OHL), work-related musculoskeletal disorders (WMSDs), and occupational stress in medical staff, and to investigate the potential associations between OHL and WMSDs as well as occupational stress. Methods A total of 880 medical staff members from 18 medical institutions were selected as the study subjects using a probability proportional method. Data on OHL, WMSDs, and occupational stress were investigated using the National Key Populations Occupational Health Literacy Surveillance Individual Questionnaire (including the Musculoskeletal Disorders Questionnaire and the Core Occupational Stress Scale), and the influencing factors and potential relationships were analyzed. Results The OHL level was 63.1%, the prevalence of WMSDs was 88.1%, and the detection rate of occupational stress was 24.0% in the study subjects. Multivariable logistic regression analysis showed that female medical staff had lower OHL levels than males (P<0.05). Medical staff with higher education levels and those working in higher-level medical institutions had higher OHL levels (all P<0.05). Medical staff working >40-<49 hours per week and those working ≥49 hours per week had higher OHL levels than those working ≤40 hours per week (all P<0.01). Medical staff with higher education levels or longer working hours had a higher risk of WMSDs (all P<0.05). Medical staff with lower monthly personal income, longer weekly working hours, and lower OHL levels had a higher risk of occupational stress (all P<0.05), and those working night shifts had a higher risk of occupational stress than those who did not (P<0.01). Conclusion The study participants had a relatively high level of OHL, a high prevalence of WMSDs, and a low detection rate of occupational stress. It is recommended that medical institutions effectively implement occupational health promotion and support measures to improve the OHL level of medical staff, to reduce the risk of WMSDs and occupational stress.

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 explore the mechanism by which aerobic exercise improves hippocampal neuroinflammation and learning and memory ability in brain-aged mice via interferon regulatory factor 7 (IRF7). MethodsA total of 60 male specific pathogen-free (SPF) C57BL/6J mice were enrolled in the experiment, including 30 young mice (2 months old) and 30 naturally aging mice (12 months old). Young mice and old mice were respectively randomly assigned to four groups: young control group (group C, n=15), young exercise group (group CE, n=15), and aging control group (group A, n=15), aging exercise group (group AE, n=15). Mice in the exercise groups received an 8-week continuous treadmill aerobic exercise intervention, while control mice were kept under routine feeding without exercise intervention. After the intervention, Barnes maze and Y-maze behavioral tests were performed to comprehensively assess the learning and memory abilities of each group of mice. Transmission electron microscopy was used to observe the ultrastructural morphological changes of hippocampal synapses. Western blot assay was utilized to detect the protein expression and phosphorylation levels of Janus kinase 1 (JAK1), signal transducer and activator of transcription 1 (STAT1), as well as the expression of postsynaptic density protein 95 (PSD95). Immunofluorescence staining was applied to quantify the fluorescence intensity of ionized calcium-binding adapter molecule 1 (IBA1) in hippocampal tissues. The expression levels of inflammatory cytokines including interferon-α (IFN-α), interferon-β (IFN-β), tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) were measured by enzyme-linked immunosorbent assay (ELISA). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the mRNA expression level of IRF7, and DNA methylation assay was further conducted to analyze the methylation level of the IRF7 gene in mouse hippocampus. ResultsAt the molecular level, compared with group C, the protein expression levels of JAK1, STAT1, p‑JAK1, and p‑STAT1 in group A were significantly increased (P<0.05), whereas PSD95 expression was decreased (P<0.05). ELISA assays showed that the levels of IFN‑α, IFN‑β, TNF‑α, and IL‑1β were significantly elevated in group A (P<0.001). RT‑PCR analysis revealed that, compared with group C, the mRNA expression of IRF7 in group A was markedly upregulated (P<0.000 1), while the CpG methylation rate of the IRF7 promoter was significantly reduced (P<0.05). In contrast, all the above parameters in the AE group showed improvements relative to those in group A. Immunofluorescence staining indicated positive IBA1 expression in the brain tissues of group A mice. Exercise intervention reversed these abnormal changes. ConclusionIn aged mice, aerobic exercise induces hypermethylation of the IRF7 promoter region, suppresses IRF7 expression and activation of the downstream IFN‑I/JAK1/STAT1 signaling pathway, accompanied by alleviated neuroinflammation, improved learning and memory, and ameliorated brain aging-related phenotypes.

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

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

Objective To analyze the factors influencing the occupational health literacy (OHL) level among workers in key industries from the perspectives of both individual workers and enterprises. Methods A total of 32 336 front-line workers from 12 key industries in the secondary industry in Guangdong Province were selected as the research subjects by a stratified cluster random sampling method. Their OHL level was investigated using Occupational Health Literacy Questionnaire of National Key Populations, and the influencing factors were analyzed. Results The OHL level of the research subjects was 48.5%. The OHL level of the research subjects in four dimensions from high to low was basic knowledge of occupational health protection, occupational health practice and behavior, legal knowledge of occupational health, and basic skills of occupational health protection (80.7%, 61.2%, 48.3% and 29.5%, respectively). The multivariable logistic regression analysis showed that the OHL level of female workers was lower than that of males (P<0.05). Lower OHL was also associated with older age, lower education level, lower personal monthly income of workers (all P<0.01). The workers with length of service < 3 years and ≥ 20 years had lower OHL level than those with length of service 3-<10 years and 10-<20 years, respectively (all P<0.05). Workers in larger enterprises had higher OHL levels (all P<0.01). The OHL level of workers in the sixth category of industries with occupational injuries had higher occupational injury risks than those in the third and fourth categories (all P<0.05). The OHL levels of workers in state-owned enterprises, private enterprises, foreign-funded enterprises, and other enterprises were higher than that of workers in public institutions (all P<0.05). Conclusion The influencing factors of workers′ OHL in key industries of the secondary industry include individual factors (gender, age, education level, personal monthly income, length of service) and enterprise factors (enterprise size, enterprise nature and industry injury risk category). Female, older workers, those with lower education or income, and those with short length of service represent priority groups for OHL interventions, while small and micro enterprises are priority units for future workplace health promotion intervention.

Various chromatographic methods were comprehensively applied to study the chemical composition of the ethyl acetate extract from Aucklandiae Radix. The structures of all compounds were identified by analyzing their physicochemical properties and using spectroscopic methods. Two new sesquiterpenoids, named auclappsines A and B(1 and 2) were isolated and identified. Through in vitro high content screening and with the use of a guggulsterone-induced L02 cells, the effects of 1 and 2 on farnesoid X receptor(FXR) protein expression were investigated. The results showed that 1 had a significant FXR activation effect, providing a scientific basis for the development of drugs for the treatment of liver and gallbladder diseases.
Receptors, Cytoplasmic and Nuclear/genetics* ; Humans ; Sesquiterpenes/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Cell Line ; Molecular Structure

This paper aimed to use non-targeted urine metabolomics to reveal the potential biomarkers of toxicity in rats with hepatic injury induced by aqueous extracts of Dictamni Cortex(ADC). Forty-eight SD rats were randomly assigned to a blank group and high-dose, medium-dose, and low-dose ADC groups, with 12 rats in each group(half male and half female), and they were administered orally for four weeks. The hepatic injury in SD rats was assessed by body weight, liver weight/index, biochemical index, L-glutathione(GSH), malondialdehyde(MDA), and pathological alterations. The qPCR was utilized to determine the expression of metabolic enzymes in the liver and inflammatory factors. Differential metabolites were screened using principal component analysis(PCA) and partial least squares-discriminant analysis(PLS-DA), followed by a metabolic pathway analysis. The Mantel test was performed to assess differential metabolites and abnormally expressed biochemical indexes, obtaining potential biomarkers. The high-dose ADC group showed a decrease in body weight and an increase in liver weight and index, resulting in hepatic inflammatory cell infiltration and hepatic steatosis. In addition, this group showed elevated levels of MDA, cytochrome P450(CYP) 3A1, interleukin-1β(IL-1β), and tumor necrosis factor-α(TNF-α), as well as lower levels of alanine transaminase(ALT) and GSH. A total of 76 differential metabolites were screened from the blank and high-dose ADC groups, which were mainly involved in the pentose phosphate pathway, tryptophan metabolism, purine metabolism, pentose and glucuronic acid interconversion, galactose metabolism, glutathione metabolism, and other pathways. The Mantel test identified biomarkers of hepatotoxicity induced by ADC in SD rats, including glycineamideribotide, dIDP, and galactosylglycerol. In summary, ADC induced hepatotoxicity by disrupting glucose metabolism, ferroptosis, purine metabolism, and other pathways in rats, and glycineamideribotide, dIDP, and galactosylglycerol could be employed as the biomarkers of its toxicity.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Biomarkers/metabolism* ; Liver/metabolism* ; Drugs, Chinese Herbal/adverse effects* ; Female ; Chemical and Drug Induced Liver Injury/metabolism* ; Glutathione/metabolism* ; Humans