Irritable bowel syndrome （IBS） is a functional bowel disorder characterized primarily by abdominal pain and altered defecation habits. In recent years， traditional Chinese medicine （TCM） has made progress in multiple aspects of IBS research and treatment， including syndrome distribution， development of TCM formulas， clinical efficacy evaluation， external therapies， and psychosocial regulation. However， it still faces challenges such as over-reliance on symptomatic manifestations rather than biomarkers for diagnostic criteria， and the lack of high-quality evidence-based data supporting the efficacy of TCM formulas in treating IBS. This paper proposed that TCM diagnosis and treatment of IBS should adhere to the strategy of integrating the holistic concept with syndrome differentiation and treatment， combining TCM external therapies such as acupuncture， moxibustion and acupoint application）， and emphasizing individualized diagnosis and treatment for psychosomatic abnormalities. Future research should integrate multi-omics technologies， artificial intelligence and other methods to deepen the understanding of the pathogenesis of IBS and the mechanisms of TCM formulas， so as to promote the standardization and internationalization of TCM in the diagnosis and treatment of IBS.

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.

Adenosine triphosphate(ATP),as the core energy metabolism molecule in living systems,has dynamic changes closely related to fundamental physiological processes.To meet the urgent demand for spatiotemporal ATP detection in vivo and in situ,the development of highly sensitive multifunctional synchronous sensing fluorescent probes has become a recent research focus.These dual-function probes achieve fluorescence detection of dual targets by designing recognition sites for ATP alongside biological factors or microenvironment parameters such as reactive oxygen/nitrogen/sulfur species,metal ions,and enzymes,enabling physiological/pathological state correlation analysis through bioimaging.This paper systematically reviews recent advances in fluorescent probes for the collaborative detection of ATP and key biomolecules.It specifically examines probe construction strategies based on specific molecular recognition mechanisms(e.g.,metal coordination competition,electrostatic interactions,and host-guest recognition),multi-modal optical signal transduction mechanisms(ratiometric fluorescence,fluorescence lifetime,and photodynamic therapy),and their applications in pathological models such as oxidative stress monitoring,metal homeostasis imbalance,and enzyme activity co-detection.Finally,from the perspective of molecular probe engineering,current challenges and future research directions are proposed to provide methodological support for precise analysis of ATP-related life process regulation networks.

Aim To investigate the effect of Huangqi injection(HI)and Huangqi oral solution(HO)on cisplatin-induced nephrotoxicity(CIN)based on un-targeted metabolomics technology and the underlying mechanisms.Methods Sprague Dawley(SD)rats were randomly divided into the blank group,cisplatin(CDDP)model group,HI treatment group,and HO treatment group,then the CIN model was built with low dose multiple intraperitoneal injections of CDDP.Pre-liminary evaluation of the renal protective efficacy of HI and HO was performed by measuring serum creatinine(Scr),blood urea nitrogen(BUN),and organ indi-ces.Further screening and identification of potential biomarkers(PBs)related to CIN and HI/HO pharma-cological effects were attained through metabolomics studies of renal tissues,and pathway enrichment analy-sis was conducted.Results HI and HO significantly restored the abnormal increase in renal function indica-tors and abnormal decrease in organ indices caused by CDDP,as well as significantly improved the abnormal renal metabolic profile induced by CDDP,indicating that both HI and HO had good alleviating effects on CIN.HI significantly reversed 47 out of 54 CIN related PBs,mainly involving metabolic pathways such as glycerophospholipid metabolism,tryptophan metabo-lism,pantothenate and CoA biosynthesis;HO signifi-cantly reversed 18 out of 54 CIN related PBs,mainly involving metabolic pathways such as taurine and hypo-taurine metabolism,ascorbate and aldarate metabo-lism,pentose and glucuronate interconversions.Con-clusions Both HI and HO have significant alleviating effects on CIN.In the short term,HI salleviating effect is superior to that of HO.Overall,the mechanisms by which both alleviate CIN are mainly related to regula-ting lipid metabolism,amino acid metabolism.

Aim To investigate the effect of Huangqi injection(HI)and Huangqi oral solution(HO)on cisplatin-induced nephrotoxicity(CIN)based on un-targeted metabolomics technology and the underlying mechanisms.Methods Sprague Dawley(SD)rats were randomly divided into the blank group,cisplatin(CDDP)model group,HI treatment group,and HO treatment group,then the CIN model was built with low dose multiple intraperitoneal injections of CDDP.Pre-liminary evaluation of the renal protective efficacy of HI and HO was performed by measuring serum creatinine(Scr),blood urea nitrogen(BUN),and organ indi-ces.Further screening and identification of potential biomarkers(PBs)related to CIN and HI/HO pharma-cological effects were attained through metabolomics studies of renal tissues,and pathway enrichment analy-sis was conducted.Results HI and HO significantly restored the abnormal increase in renal function indica-tors and abnormal decrease in organ indices caused by CDDP,as well as significantly improved the abnormal renal metabolic profile induced by CDDP,indicating that both HI and HO had good alleviating effects on CIN.HI significantly reversed 47 out of 54 CIN related PBs,mainly involving metabolic pathways such as glycerophospholipid metabolism,tryptophan metabo-lism,pantothenate and CoA biosynthesis;HO signifi-cantly reversed 18 out of 54 CIN related PBs,mainly involving metabolic pathways such as taurine and hypo-taurine metabolism,ascorbate and aldarate metabo-lism,pentose and glucuronate interconversions.Con-clusions Both HI and HO have significant alleviating effects on CIN.In the short term,HI salleviating effect is superior to that of HO.Overall,the mechanisms by which both alleviate CIN are mainly related to regula-ting lipid metabolism,amino acid metabolism.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Attention refers to the body's capacity to maintain alert awareness of internal and external stimuli.Attention deficit can impair an individual's learning capacity and working proficiency,resulting in a heavy burden to the family and society.Vagus nerve stimulation(VNS)is an emerging neuro regulatory technique that can achieve reciprocal interaction between the central and peripheral nervous systems,which has demonstrated great potential for the treatment of neurological and psychiatric diseases;however,it has not been used clinically to treat attention-deficient developmental disorders in children or neurodegenerative diseases in the aged population.We briefly review the anatomical and physiological bases of VNS,and its application status and potential use in treating attention deficit diseases.A potential mechanism by which VNS participates in attention-related nervous regulation is proposed,to lay a theoretical foundation for the application of VNS in the field of attention nervous regulation.

Ergonomic Principle for the Prevention of Work-Related Musculoskeletal Disorders Part 1: General Principles (T/WSJD 14.1-2020) is the first guideline standard for the prevention of work-related musculoskeletal disorders provided to employers and occupational health technical service institutions in China, which covers ergonomic risk factors and their hazard definition, ergonomic prevention principles, risk assessment process, risk control process and risk evaluation principles. The standard specifically clarify the specific types of adverse ergonomic risk factors and control strategies and the standard process of systematic ergonomic risk assessment, providing implementation paths and practical guidance for eliminating/reducing adverse ergonomic risks and enhance workplace environments to prevent work-related musculoskeletal disorders. This paper interprets and analyzes the background of standard establishment, formulation process, fundamental basis, and main content, etc., to provide scientific and accurate technical support for enterprise managers, labours and technical personnel of occupational health institutions to optimize the use of this standard.