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 association between the concentration of maternal serum biomarkers， including free beta-human chorionic gonadotropin （fβ-hCG）， alpha fetoprotein （AFP）， unconjugated estriol （uE3）， and inhibin A （INH A） during the second trimester pregnancy and the risk of fetal presenting with copy number variation （CNV）. MethodsA retrospective analysis was conducted on the diagnostic results of pregnant women who were identified as high risk in the second trimester quadruple serological screening. These pregnant women underwent the interventional prenatal diagnosis and performed amniotic fluid karyotype analysis and comparative genomic hybridization array （aCGH） or CNV sequencing （CNV-seq） simultaneously.The detection rates of different diagnostic methods were calculated. The concentration of serum biomarkers， fβ-hCG， AFP， uE3， and INH A were divided into three levels： abnormally low， normal， and abnormally high. The prevalence of these serum biomarkers concentration in pregnant women with aberrant aCGH/CNV-seq results compared to normal controls was statistically calculated. ResultsAmong the 1 516 cases with high-risk quadruple serological screening in the second trimester， 59 chromosomal abnormalities were revealed by karyotype analysis， while 87 abnormalities were detected by aCGH/CNV-seq， indicating a significant statistical difference in detection rate. In comparison to the result of karyotype analysis， aCGH/CNV-seq identified an additional 46 cases of fetal CNV abnormalities， whereas it failed to detect 18 cases of chromosomal structural abnormalities. Among the 1 457 cases with normal karyotype results， the most frequent abnormality was observed in the concentration of fβ-hCG， followed by abnormalities in INH A and uE3 concentrations， yet the least common abnormality was noted in AFP concentration. When the maternal serum uE3 concentration was less than 0.5 multiples of median （MoM）， there was a significant difference in the risk of fetal CNV compared to the CNV results corresponding to normal concentration of uE3. No significant differences were found in the levels of fβ-hCG， AFP， and INH A between the two groups. ConclusionLow serum uE3 concentration （<0.5 MoM） is associated with an increased risk of fetal presenting with aberrant CNV.

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.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

With the completion of the "Human Genome Project" and the smooth progress of the "Herbal Genome Project", the research wave of RNAomics is gradually advancing, opening the research gateway for the modernization of traditional Chinese medicine (TCM) and initiating the post-genome era of medicinal plant RNA research. Therefore, this article proposes for the first time the concept of HerbRNomes, which involves constructing databases of medicinal plant, medicinal fungus, and medicinal animal RNA at different stages, from different origins, and in different organs. This research aims to explore the role of HerbRNA in self-genetic information transmission, functional regulation, as well as cross-species regulation functional mechanisms and key technologies. It also investigates application scenarios, providing a theoretical basis and research ideas for the resistance of TCM or medicinal plants to adversity and stress, molecular assistant breeding, and the development of small nucleic acid drugs. This article reviews recent research progress in elucidating the molecular mechanisms of the transmission and expression of genetic information, self-regulation and cross-species regulation of herbs at the RNA level, along with key technologies. It proposes a development strategy for small nucleic acid drugs based on HerbRNomes, providing theoretical support and guidance for the modernization of TCM based on HerbRNomes research.

This paper systematically summarizes the practical experience of the 2025 Dingri earthquake emergency medical rescue in Tibet. It analyzes the requirements for earthquake medical rescue under conditions of high-altitude hypoxia, low temperature, and low air pressure. The paper provides a detailed discussion on the strategic layout of earthquake medical rescue at the national level, local government level, and through social participation. It covers the construction of rescue organizational systems, technical systems, material support systems, and information systems. The importance of building rescue teams is emphasized. In high-altitude and cold conditions, rapid response, scientific decision-making, and multi-party collaboration are identified as key elements to enhance rescue efficiency. By optimizing rescue organizational structures, strengthening the development of new equipment, and promoting telemedicine technologies, the precision and effectiveness of medical rescue can be significantly improved, providing important references for future similar disaster rescues.

AIM To investigate the effects of Yiqi Juanbi Formula on chondrocyte pyroptosis in rat models of collagen-induced arthritis(CIA).METHODS Fifty rats were subcutaneously injected at the tail base with an emulsion containing equal volumes of bovine type Ⅱ collagen and incomplete Freund's adjuvant(IFA)to establish the CIA models.These rats were then randomly assigned to the model group,the methotrexate group(0.35 mg/kg),and the low-dose,medium-dose,and high-dose Yiqi Juanbi Formula groups(9.4,18.7,37.4 g/kg),in contrast to the ten intact rats serving in the normal control group.Following four weeks of intragastric administration,the rats had their general conditions observed;their joint swelling and arthritis indices measured;their ankle joint pathology assessed by HE staining;their serum levels of IL-1β,IL-18 and TNF-ɑ detected by ELISA;their mRNA expressions of NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ in ankle cartilage quantified by RT-qPCR;their protein expressions of NF-κB,NLRP3 and Caspase-1 in ankle cartilage analyzed by Western blot;and their NLRP3 and GSDMD positive expressions in ankle cartilage examined by immunohistochemistry.RESULTS Compared to the control group,the model group showed significantly increased joint swelling and arthritis indices(P＜0.01);elevated serum levels of IL-1 β,IL-18 and TNF-ɑ(P＜0.01);pathological changes including cartilage surface defects,reduced cell count,altered cellular morphology,irregular cell arrangement,and significant inflammatory cell infiltration in synovial tissue;upregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.01)and increased protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.01)in ankle cartilage;enhanced positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).Compared to the model group,the groups intervened with methotrexate or medium-or high-dose Yiqi Juanbi Formula exhibited reduced joint swelling and arthritis indices(P＜0.01);alleviated pathological damage in ankle joints;decreased serum levels of IL-1β,IL-18 and TNF-ɑ(P＜0.01);downregulated mRNA expressions of NF-κB,NLRP3,Caspase-1,GSDMD,IL-1β,IL-18 and TNF-ɑ(P＜0.05,P＜0.01),and reduced protein expressions of NF-κB,NLRP3 and Caspase-1(P＜0.05,P＜0.01)in ankle cartilage;and diminished positive expressions of NLRP3 and GSDMD in ankle cartilage(P＜0.01).CONCLUSION Yiqi Juanbi Formula alleviates inflammation in CIA rats,potentially by inhibiting the activation of the NF-κB/NLRP3/Caspase-1 signaling pathway,thereby suppressing chondrocyte pyroptosis.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

Objective To develop a foot pad for arch support after plantar skin grafting and evaluate its rehabilitation effect on scar tissue in the arch area of postoperative patients.Methods The foot pad for arch support after plantar skin grafting was fabricated from medical-grade silicone and comprised a pad body and an arch support component.The pad body featured shock-absorbing convex patterns on its surface,precision-cut grooves in the metatarsal region and an upwardly convex arch section;the arch support component consisted of an arch-supporting portion with heel reinforcement and a heel-supporting portion,both peripherally integrated with vibration-damping through-holes.Totally 82 burn patients undergoing grafting using plantar skin were selected and divided equally into a control group and an experimental group with the random number table method.In the control group,anti-scarring care such as applying silicone gel and pressure therapy by rehabilitation nurses was carried out immediately after the healing of the plantar skin removal site.In the experimental group,the foot pad was used for rehabilitation care besides the routine treatment in the control group.The two groups were compared in terms of scarring and adverse reactions at the plantar skin removal site.SPSS 25.0 software was used for statistical analysis.Results Within 1 week after healing,the number of patients in the experimental group who had subcutaneous bruising and rupture was less than that in the control group,and the difference was statistically significant(P＜0.05).At 3 months after healing,the experimental group behaved better than the control group in scarring,and the difference was statistically significant(P＜0.05).Conclusion The foot pad developed with simple structure and easy operation can be used for rehabilitation exercise of patients after plantar skinning.[Chinese Medical Equipment Journal,2025,46(3):115-117]

Objective:To investigate the expression of LINC01088 in breast cancer and its effects on cell prolifera-tion,migration,and invasion.Methods:GEPIA and bc-GenExMiner were used to analyze the correlation between LINC01088 expression levels and clinical characteristics as well as prognosis.The expression of LINC01088 in MCF10A and MDA-MB-231,BT-549,MCF7 were detected by Real-time PCR.The effect of LINC01088 on the biological func-tion of breast cancer cells was examined by overexpressing LINC01088 in breast cancer cells.Cell proliferation was as-sessed using the Incucyte assay,while cell migration and invasion were evaluated using Transwell assays.Western blot-ting was employed to detect the expression of proteins associated with cell proliferation and metastasis.Results:LINC01088 expression was significantly lower in breast cancer tissues compared to normal breast tissues(P<0.05).Data from the bc-GenExMiner database revealed higher LINC01088 expression in HER2 positive patients(P<0.0001),corre-lating with longer overall survival(P=0.0006)and disease-free survival(P=0.0002).The mRNA expression level of LINC01088 in normal breast epithelial cell line was higher than that in breast cancer cell lines(P<0.05).Overexpression of LINC01088 significantly reduced proliferation,migration,and invasion in three breast cancer cell lines(P<0.01).Addi-tionally,LINC01088 upregulated p21 and p27(P<0.01),while downregulating Snail,Slug,PI3K,and phosphorylated Akt(P<0.05).Conclusion:LINC01088 expression was significantly reduced in human breast cancer.In vitro,LINC01088 in-hibited the proliferation,migration,and invasion of breast cancer cells.This effect may be attributed to its role in sup-pressing the PI3K-AKT pathway and epithelial-mesenchymal transition.