Cardiotoxicity induced by antineoplastic agents is an important factor affecting the prognosis of cancer patients. This article systematically summarizes the clinical manifestations of cardiotoxicity caused by chemotherapeutic drugs, molecular targeted cancer therapies, and immunotherapeutic drugs, and identifies the core injury mechanisms, including cardiomyocyte apoptosis, oxidative stress, and mitochondrial dysfunction. In Traditional Chinese Medicine (TCM) theory, anticancer drug-induced cardiotoxicity falls under the category of “heat toxins” characterized by dual deficiency of qi and blood, upward flaming of heart heat, stagnation of the heart vessels, and meridian obstruction induced by phlegm-dampness. Therefore, by reviewing the preventive and therapeutic effects and the underlying mechanisms of tonic medicines, interior heat-clearing medicines, blood-circulating and stasis-resolving medicines, phlegm-transforming, cough-suppressing and panting-relieving medicines, and interior-warming medicines, this paper concludes that TCM prevention and treatment of this condition adheres to the formulation principle of “reinforcing healthy qi, removing toxins, and resolving blood stasis”. Its mechanisms of action—including antioxidation effects, regulation of autophagic balance, antagonism of inflammatory responses, and improvement of microcirculation—are highly consistent with modern medical treatment strategies (e.g., dexrazoxane for inhibiting iron-dependent oxidative stress, statins for anti-inflammatory effects). This study provides theoretical support and practical guidance for the collaborative and precise prevention and treatment of anticancer drug-induced cardiotoxicity through the integration of traditional Chinese and Western medicine.

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.

Drug impurity control is essential for ensuring pharmaceutical quality.In this study,an unknown impurity at relative retention time(RRT)of 0.45 in Torasemide injection was systematically investigated using high performance liquid chromatography-ion trap time-of-flight mass spectrometry(LC-IT-TOF-MS/MS),forced degradation studies,and nuclear magnetic resonance(NMR)spectroscopy,aiming to elucidate its chemical structure,propose a formation mechanism,and establish a control strategy.By analysis of the ultraviolet absorption spectrum and high-resolution MS1 and MS2 data of the impurity,its possible structure was postulated.Subsequently,the target impurity was prepared via forced degradation experiment,and its structure was confirmed by NMR spectroscopy.A detailed analysis of its formation mechanism revealed that Torasemide first hydrolyzed in solution to form Torasemide sulfonamide,and then underwent condensation,dehydration,and cyclization reactions with trace amounts of formaldehyde in the excipient polyethylene glycol(PEG)400 to generate the impurity.It was experimentally proven that the target impurity was related to the residual oxygen content in Torasemide injection.Ultimately,by optimizing the prescription process,the target impurity level was controlled below the individual impurity limit of the final product(≤0.3%).This study provided valuable insights for enhancing the quality control of Torasemide injection.

Aim To investigate the effect of high-fat diet on the tight junction function injury of Sertoli cells through the NF-κB/NLRP3 signaling pathway in mice and to explore the underlying mechanism.Methods Male C57BL/6J mice were fed with high-fat or normal diet for five months.The body and gonadal organ weight of mice were measured,and their indices were calculated.The sperm concentration,the sperm viabili-ty,the testicular histomorphology and the expression levels of tight junction proteins ZO-1,Occludin and Claudin-11 were measured.TM4 cells were treated with palmitic acid(PA)for 24 h.Cell viability was detected by CCK-8 method.Then,TM4 cells were di-vided into different groups treated with PA(0,50,100,200 and 300 μmnol·L-1),and the expression lev-els of tight junction proteins ZO-1,Occludin and Clau-din-11 were detected by Western blot.The tight junc-tion permeability of TM4 cells were detected by transepithelial electrical resistance(TEER)and FITC-dextran.The expression levels of mRNA and proteins for the NF-κB/NLRP3 pathway-related factors were de-tected by RT-qPCR and Western blot.Results The results from animal experiments showed that high-fat diet increased body weight and seminal vesicle weight of mice,and decreased testicular index,epididymal in-dex,sperm concentration and sperm motility of mice.High-fat diet also caused testicular tissue structure damage and down-regulated the expression levels of tight junction proteins ZO-1 and Occludin,without af-fecting the expression of Claudin-11.In vitro,PA sig-nificantly down-regulated the expression levels of ZO-1,Occludin and Claudin-11 in TM4 cells,increased the cell permeability,as well as up-regulated the mRNA and protein expression levels of NLRP3/NF-κB signa-ling pathway-related factors in TM4 cells.Conclusions High-fat diet can impair the function of tight junction of testicualr Sertoli cells,and the machanism may be related to the activation of the NF-κB/NLRP3 signaling pathway,resulting in Sertoli cell inflammation in mice.

Aim To investigate the effect of high-fat diet on the tight junction function injury of Sertoli cells through the NF-κB/NLRP3 signaling pathway in mice and to explore the underlying mechanism.Methods Male C57BL/6J mice were fed with high-fat or normal diet for five months.The body and gonadal organ weight of mice were measured,and their indices were calculated.The sperm concentration,the sperm viabili-ty,the testicular histomorphology and the expression levels of tight junction proteins ZO-1,Occludin and Claudin-11 were measured.TM4 cells were treated with palmitic acid(PA)for 24 h.Cell viability was detected by CCK-8 method.Then,TM4 cells were di-vided into different groups treated with PA(0,50,100,200 and 300 μmnol·L-1),and the expression lev-els of tight junction proteins ZO-1,Occludin and Clau-din-11 were detected by Western blot.The tight junc-tion permeability of TM4 cells were detected by transepithelial electrical resistance(TEER)and FITC-dextran.The expression levels of mRNA and proteins for the NF-κB/NLRP3 pathway-related factors were de-tected by RT-qPCR and Western blot.Results The results from animal experiments showed that high-fat diet increased body weight and seminal vesicle weight of mice,and decreased testicular index,epididymal in-dex,sperm concentration and sperm motility of mice.High-fat diet also caused testicular tissue structure damage and down-regulated the expression levels of tight junction proteins ZO-1 and Occludin,without af-fecting the expression of Claudin-11.In vitro,PA sig-nificantly down-regulated the expression levels of ZO-1,Occludin and Claudin-11 in TM4 cells,increased the cell permeability,as well as up-regulated the mRNA and protein expression levels of NLRP3/NF-κB signa-ling pathway-related factors in TM4 cells.Conclusions High-fat diet can impair the function of tight junction of testicualr Sertoli cells,and the machanism may be related to the activation of the NF-κB/NLRP3 signaling pathway,resulting in Sertoli cell inflammation in mice.

Objective:To evaluate the potential value of 18×10 3 translocator protein (TSPO) radioligand ( N, N-diethy1-2-(2-(4-(2- 18F-fluoroethoxy) phenyl)-5, 7-dimethylpyrazolo[1, 5-A]pyrimidin-3-yl)acetamide, 18F-DPA-714) PET compared with conventional MR in the detection of autoimmune encephalitis (AE), the correlation with clinical symptoms, and the monitoring of immunotherapy efficacy in patients with AE. Methods:From December 2021 to June 2024, 45 AE patients (17 males, 28 females, age (38.3±17.0) years) diagnosed at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine and 10 healthy volunteers (7 males, 3 females, age (28.7±5.1) years) were enrolled in this prospective study. All participants underwent baseline 18F-DPA-714 PET/MR scans, and 23 of these AE patients underwent further follow-up 18F-DPA-714 PET/MR scans. 18F-DPA-714 PET positivity was defined as having an uptake intensity threshold higher than the mean SUV ratio (SUVR)+ 2 s of the corresponding brain region in healthy controls. MR positivity was defined as abnormal hyperintensity in a specific brain region or multiple brain regions on the T 2 fluid attenuated inversion recovery (FLAIR). The positive detection rates of 18F-DPA-714 PET and MR was analyzed using McNemar χ2 test, and the differences in the uptake intensity (SUVR) of 18F-DPA-714 between symptomatic and non-symptomatic groups, and between remission and non-remission groups after immunotherapy were compared using independent-sample t test or Wilcoxon rank sum test. Spearman rank correlation analysis was used to analyze the correlation between the changing rate of SUVR and the changing of the modified Rankin Scale (mRS) score before and after treatment. Results:The positive detecting rate of 18F-DPA-714 PET for AE was significantly higher than that of MR (73.3%(33/45) vs 35.6%(16/45); χ2=11.56, P=0.001). The cerebellar SUVR of ataxia patients was significantly higher than that of asymptomatic patients (1.22(1.06, 1.33) vs 1.08(0.99, 1.20); Z=-2.14, P=0.034). Follow-up imaging showed that the SUVR of patients in the remission group after immunotherapy was significantly lower than that in the non-remission group ((-15.19±10.17)% vs (14.26±13.36)%; t=5.81, P<0.001). There was a significant correlation between the changing rate of SUVR and the changing of the mRS score before and after treatment ( rs=0.65, P<0.001). Conclusion:Compared with conventional MR, 18F-DPA-714 PET has a higher positive detecting rate for AE, and has the potential to reflect the clinical symptoms of AE and monitor the efficacy of immunotherapy.

The scaphoid bone is one of the important bone of hand,which is frequently injured and difficult to treat in clinical practice.Therefore,it is very important to deeply study the microstructure and biomechanical characteristics of the scaphoid bone for understanding its injury mechanism and optimizing treatment scheme.Microcomputed tomography(micro-CT)provides high-resolution imaging of bone tissue,while finite element analysis can help to simulate the stress distribution and behavioral patterns of the scaphoid bone under various physiological and pathological states.The high-resolution three-dimensional image of the scaphoid bone obtained by micro-CT technology can be used to construct finite element models of real anatomical structure of the scaphoid bone,thus achieving accurate simulation of the mechanical properties of the scaphoid bone.The fusion of these two advanced technologies provides a new perspective for revealing the structural and functional relationships and injury mechanism of the scaphoid bone.Therefore,this paper reviews the anatomical characteristics of the scaphoid bone and its biomechanical behavior in different states,emphasizing the specific applications and advantages of micro-CT and finite element analysis techniques in the study of the scaphoid bone.By summarizing the research findings in recent years,this paper provides novel scientific basis and methods for the diagnosis,treatment,and prevention of scaphoid bone-related disorders.

Cervical esophageal cancer(CEC)is a relatively rare pathological subtype of esophageal cancer,accounting for 2%～10%of all esophageal cancer cases,95%of which are esophageal squamous cell carcinoma.Due to nonspecific clinical symptoms,most CEC patients are diagnosed at advanced or locally advanced stages,resulting in a 5-year overall survival(OS)rate of approximately 30%.Although therapeutic outcomes for CEC patients remain suboptimal,current studies have demonstrated the clinical value of multiple treatment modalities,including endoscopic therapy,photodynamic therapy,selective or salvage surgery,radiotherapy,chemotherapy,concurrent chemoradiotherapy(CCRT),immunotherapy,and targeted therapy.This article systematically reviews recent advances in CEC management,focusing on the aforementioned approaches,and delves into the optimal treatment strategies,aiming to provide a scientific and reliable foundation for clinical practise.