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.

Core fucosylation, catalyzed exclusively by fucosyltransferase 8 (FUT8), is an evolutionarily conserved post-translational modification that has emerged as a central regulatory hub linking liver homeostasis, chronic disease progression, and malignant transformation. Liver diseases, particularly hepatocellular carcinoma, remain a leading global health burden characterized by late diagnosis, limited therapeutic options, and poor overall survival. While aberrant glycosylation is now recognized as a hallmark of cancer and inflammatory disorders, existing research on FUT8-mediated core fucosylation in liver diseases remains fragmented: the dynamic functional switch of FUT8 from a homeostatic regulator to a pathological driver across the full disease continuum has not been systematically delineated, and the integrated mechanisms by which core fucosylation modulates oncogenic signaling, metabolic reprogramming, and immune evasion remain poorly understood. This review synthesizes recent advances to establish a unified framework for understanding the dual role of core fucosylation in liver physiology and pathology, and evaluates its translational potential for precision medicine. At the molecular level, FUT8’s unique catalytic specificity makes core fucosylation an irreplaceable modification, as evidenced by the perinatal lethality and severe organ dysfunction in Fut8 knockout mice. In hepatocellular carcinoma, genomic amplification of guanosine 5'-diphosphate-fucose biosynthetic enzymes provides metabolic support for aberrant core fucosylation. FUT8 expression is tightly regulated by a multi-layered network: transcriptional activation via Wnt/β‑catenin and wild-type p53, epigenetic upregulation by lncRNAs, post-transcriptional repression by miR-122-5p and miR-34a, and virus-specific induction by hepatitis B virus/hepatitis C virus. Physiologically, core fucosylation maintains liver homeostasis through four core mechanisms: it acts as a molecular switch for epidermal growth factor receptor/hepatocyte growth factor receptor signaling to enable liver regeneration; directs polarized secretion of hepatocyte-derived glycoproteins into bile ducts; modulates cholesterol metabolism via the hepatocyte nuclear factor 1α-proprotein convertase subtilisin/kexin type 9-low density lipoprotein receptor axis; and regulates aging through insulin‑like growth factor 1 receptor signaling. Pathologically, core fucosylation exhibits context-dependent dual functions: in liver fibrosis, FUT8 upregulation in hepatic stellate cells forms a negative feedback loop that limits excessive fibrogenesis; in hepatocellular carcinoma, however, aberrant FUT8 overexpression drives cell-autonomous malignancy by constitutively activating epidermal growth factor/hepatocyte growth factor receptor, transforming growth factor‑β/Smad, and Wnt/β‑catenin pathways, while simultaneously establishing a multi-layered immune evasion network by stabilizing programmed cell death ligand 1 and cluster of differentiation 47, and impairing natural killer cell homeostasis via interleukin‑2 receptor β glycosylation. Clinically, stage-specific core fucosylation biomarkers enable non-invasive monitoring of liver disease progression: low molecular mass kringle-Fc fusion protein outperforms conventional markers for early fibrosis detection, while alpha-fetoprotein-L3 and novel glycopeptides (α‑2‑macroglobulin N‑linked glycosylation site 1424, lumican core fucosylated peptide) significantly improve early hepatocellular carcinoma diagnosis, especially in alpha-fetoprotein-negative patients. Next-generation detection technologies (chemoenzymatic labeling, site-specific mass spectrometry) overcome the specificity limitations of traditional lectin assays. Therapeutically, four promising strategies are emerging: small-molecule FUT8 inhibitors, afucosylated antibodies with enhanced antibody‑dependent cellular cytotoxicity, Fuc-modified targeted drug delivery systems, and core fucose-specific lectins for NASH treatment. The core challenge for clinical translation lies in FUT8’s inherent “double-edged sword” effect, as systemic inhibition disrupts its essential physiological functions beyond pathological roles. Long-term systemic FUT8 blockade not only impairs post-injury liver regeneration by abrogating epidermal growth factor/hepatocyte growth factor receptor signaling but also disrupts cholesterol homeostasis via the hepatocyte nuclear factor 1α-proprotein convertase subtilisin/kexin type 9-low density lipoprotein receptor axis, leading to dyslipidemia and altered bile secretion. Critically, it compromises immune surveillance by destabilizing interleukin‑2 receptor β on natural killer cells, reducing their cytotoxic activity against malignant and virally infected cells, and impairs IgG Fc-mediated effector functions, increasing susceptibility to infections. This fundamental trade-off between therapeutic efficacy and systemic toxicity necessitates a paradigm shift from non-specific global inhibition to precision modulation of pathological core fucosylation. By addressing these critical challenges, FUT8-mediated core fucosylation has the potential to transform liver disease management from late-stage intervention to early detection and precision therapy, ultimately improving patient outcomes and reducing the global burden of liver diseases.

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.

Acute pancreatitis(AP)is a common digestive disorder associated with moderate to high nutritional risks,necessitating timely nutritional support.Over the past five decades,medical nutrition therapy for AP has undergone a paradigm shift,transitioning from traditional fasting based on the"pancreatic rest theory"to the current emphasis on early enteral feeding to"awaken the gut."Currently,nutritional treatment has become a cornerstone of comprehensive AP management.The European Society for Clinical Nutrition and Metabolism(ESPEN),founded in 1980,is a leading professional organization dedicated to advancing research,clinical practice,and education in clinical nutrition and metabolism.To date,ESPEN has published five evidence-based guidelines on nutritional management in pancreatic diseases.This article reviews the evolution of AP nutritional therapy as outlined in these ESPEN guidelines,highlighting key recommendations and their clinical implications.

The traditional detection methods for monitoring the biomass,protein,chlorophyll content and other key indicators in the growth of chlorella have some problems,including complicated operation,slow detection speed and difficult large-scale application.In this study,a fast and efficient monitoring method for the key indicators in the growth of chlorella was established using near infrared spectroscopy and chemometrics.Near-infrared spectroscopy was used to collect near-infrared spectra of chlorella algal fluid at different growth stages,and standard methods were used to detect the biomass,protein and chlorophyll contents of corresponding samples.A quantitative analysis model was established based on partial least squares regression(PLSR).To improve the prediction ability of the model,multiplicative scatter correction(MSC)was used to reduce the interference of scattering on the raw spectrum(RS),standard normal variate(SNV)was used to normalize the original spectral data to eliminate differences between samples,continuous wavelet transform(CWT)was used to obtain the key features of spectral data,the first derivative(1st)was used to enhance the differentiation of the original spectral features,and monte carlo-uninformative variable elimination(MC-UVE)and randomization test(RT)were used to screen the valid variables in the wavelength.By evaluating the prediction ability of different models,the quantitative analysis models of chlorella biomass,protein and chlorophyll content were finally determined.The results showed that the model based on 1st combined with RT spectra had better predictive ability for chlorella nutrient content detection,and the root mean square errors of prediction(RMSEP)and coefficients of determination(R2)were 0.041 and 0.933 for biomass,0.012 and 0.973 for protein,and 0.517 and 0.962 for chlorophyll,respectively.This model showed practical application value,and could realize the rapid and accurate detection of chlorella biomass,protein and chlorophyll content at the same time.

Objective To verify the efficacy of a domestic human DNA quantification kit based on real-time fluorescence quantitative PCR in detecting the total human DNA concentration,male DNA concen-tration in mixed male/female DNA samples,the degree of DNA degradation and inhibitor tolerance.Methods Samples with different concentrations,different male/female ratios,different concentrations of inhibitors,and different degradation degrees were tested using the domestic human DNA quantification kit based on real-time fluorescence quantitative PCR.This kit was compared with a similar product on the market and was applied to the detection of DNA from real cases.Results This human DNA quan-tification kit can effectively detect human DNA as low as 0.001 65 ng/μL,and 6.25 pg/μL of male DNA in mixed samples with a male-to-female ratio of 1∶15 000.Even when the sample contains as high as 400 ng/μL of humic acid or 1 000 μmol/L of hemin alone,the DNA concentration can still be accurately detected.The degradation index can effectively characterize the degradation degree of the sample.This kit has been successfully applied in forensic practice.Conclusion This human DNA quan-tification kit is accurate and reliable in detection.It can accurately reflect the degradation of DNA and inhibitor tolerance.It has good performance in quantitative accuracy,determination of the male/female ratio in mixed samples,and inhibitor tolerance.It has application potential in forensic case examination.

Testicular descent occurs in two consecutive stages: the transabdominal stage and the inguinoscrotal stage. Androgens play a crucial role in the second stage by influencing the development of the gubernaculum, a structure that pulls the testis into the scrotum. However, the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated. To identify the androgen-regulated genes, we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout ( Lhcgr KO) mice, an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency. We found that the expression of secreted protein acidic and rich in cysteine (SPARC)-related modular calcium binding 1 ( Smoc1 ) was the most severely suppressed at both the transcript and protein levels, while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice. The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist, flutamide. In addition, in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells. In the cultures of myogenic differentiation medium, both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7 ( Pax7 ) and myogenic factor 5 ( Myf5 ). After short-interfering RNA-mediated knocking down of Smoc1 , the expression of Pax7 and Myf5 diminished, and testosterone alone did not recover, but additional SMOC1 did. These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.
Animals ; Male ; Mice ; Testis/growth & development* ; Mice, Knockout ; Androgens/pharmacology* ; Testosterone/pharmacology* ; Receptors, LH/metabolism* ; Calcium-Binding Proteins/metabolism*

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion is one of the most commonly used supportive treatments for children with hematological diseases. This guideline provides guidance and recommendations for blood transfusions in children with aplastic anemia, thalassemia, autoimmune hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, acute leukemia, myelodysplastic syndromes, immune thrombocytopenic purpura, and thrombotic thrombocytopenic purpura. This article presents the evidence and interpretation of the blood transfusion provisions for children with hematological diseases in the "Guideline for pediatric transfusion", aiming to assist in the understanding and implementing the blood transfusion section of this guideline.
Humans ; Child ; Hematologic Diseases/therapy* ; Blood Transfusion/standards* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion for children undergoing hematopoietic stem cell transplantation is highly complex and challenging. This guideline provides recommendations on transfusion thresholds and the selection of blood components for these children. This article presents the evidence and interpretation of the transfusion provisions for children undergoing hematopoietic stem cell transplantation, with the aim of enhancing the understanding and implementation of the "Guideline for pediatric transfusion".
Humans ; Hematopoietic Stem Cell Transplantation ; Child ; Blood Transfusion/standards* ; Practice Guidelines as Topic