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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

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.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

AIM To investigate the effects of Heixiaoyao Powder on neuroinflammation in APP/PS1 transgenic mice.METHODS The 16-week-old male APP/PS1 transgenic mice were randomly divided into the model group,the BBG group(P2X7R specific antagonist,30 mg/kg)and high,medium and low dose Heixiaoyao Powder groups of(22.10,11.05,5.53 g/kg),in contrast to the male C57BL/6J mice of the same age and the same strain of the blank groups,with 12 mice in each group.When normal saline by gavage was dosed upon the blank group and the model group,and the other groups were treated with corresponding drug by gavage.After 90 days of administration,the mice had their learning and memory ability detected by Morris water maze test;their hippocampal pathological changes observed with HE staining;their MyD88 expression detected by immunofluorescence staining;their hippocampal levels of pro-inflammatory factors(TNF-α,IL-6),anti-inflammatory factors(IL-10),ATP and amyloid protein β(Aβ)detected by ELISA;their hippocampal mRNA expressions of P2X7R,TLR4,MyD88 and NF-κB-P65 detected by RT-qPCR method;and their hippocampal protein expressions of P2X7R,TLR4,MyD88,NF-κB-P65 and p-NF-κB-P65 detected by Western blot.RESULTS Compared with the blank group,the model group demonstrated prolonged escape latency and reduced frequency in crossing platform(P＜0.01);decreased number of hippocampal neurons,deranged neurons,and darker cytoplasm staining;increased immunofluorescence expression of hippocampal MyD88(P＜0.01);decreased IL-10 level(P＜0.01);increased levels of TNF-α,IL-6,ATP and Aβ(P＜0.01);increased mRNA and protein expressions ofP2X7R,TLR4 and MyD88(P＜0.01),and increased protein expression of p-NF-κB-P65(P＜0.01).Compared with the model group,the groups intervened with Heixiaoyao Powder or BBG demonstrated shortened escape latency and increased frequency in crossing platform(P＜0.01);more number of neatly arranged hippocampal neurons;increased hippocampal IL-10 level(P＜0.01),decreased levels of TNF-α,IL-6,ATP and Aβ(P＜0.05,P＜0.01);decreased mRNA and protein expressions of P2X7R,TLR4 and MyD88(P＜0.05,P＜0.01);and decreased protein expression of p-NF-κB-P65(P＜0.05,P＜0.01).Except the low dose Heixiaoyao Powder group,the other treatment groups shared decreased immunofluorescence expression of MyD88(P＜0.05,P＜0.01).CONCLUSION Heixiaoyao Powder can significantly improve the learning and memory ability of APP/PS1 model mice,and its mechanism may lie in its function in alleviating cerebral neuroinflammation by reducing the abnormal Aβ aggregation via inhibiting the activation of ATP/P2X7R/NF-κB signaling pathway.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Alzheimer's disease (AD) is the commonest neurodegenerative disease that causes memory decline, cognitive dysfunction and behavior disorders in the aged people. Primary pathological hallmarks of AD include amyloid-β (Aβ), neurofibrillary tangles (NFTs), gliosis, and neuronal loss. Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels have important physiological functions, especially in aspects of controlling the resting membrane potential, pacemaker activity, memory formation, sleep and arousal. This article reviews the structure, distribution, regulation of HCN channels and the role of HCN channels in the pathological mechanisms of AD, aiming to provide drug therapeutic targets for the prevention and treatment of AD.
Humans ; Alzheimer Disease/physiopathology* ; Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels/physiology* ; Animals ; Amyloid beta-Peptides/metabolism*

Astragalus membranaceus(A. membranaceus), a traditional tonic, contains flavonoids as one of its main bioactive components and key indicators for quality standard detection. These compounds predominantly exist in glycosylated forms after glycosylation modification within the plant. The catalytic products of flavonoid glycosyltransferases in A. membranaceus have been reported to be mostly monoglycosides, and only AmUGT28 catalyzes luteolin to form diglycosides. In this study, we cloned a glycosyltransferase gene, AmGT90, from A. membranaceus, with an ORF length of 1 335 bp, encoding 444 amino acids, and the protein had a relative molecular mass of 50.5 kDa. Phylogenetic tree analysis indicated that AmGT90 belongs to the UGT74 family. In vitro enzymatic reaction showed that AmGT90 had broad substrate specificity and could catalyze the glycosylation of various flavonoids, including isoflavones, flavones, flavanones, and chalcones. AmGT90 not only catalyzed the formation of monoglycosides but also diglycosides. In addition, the mechanism of AmGT90 catalyzing the formation of diglycosides from luteolin was preliminarily explored. The experimental results showed that AmGT90 may preferentially recognize C4'-OH of luteolin and then recognize C7-OH to form diglycosides. This study reported a glycosyltransferase from A. membranaceus capable of converting flavonoids into monoglycosides and diglycosides. This finding not only enhances our understanding of the biosynthetic pathways of flavonoid glycosides in A. membranaceus but also introduces a new component for glycoside production through synthetic biology.
Glycosyltransferases/chemistry* ; Flavonoids/chemistry* ; Astragalus propinquus/classification* ; Phylogeny ; Glycosylation ; Plant Proteins/chemistry* ; Substrate Specificity ; Cloning, Molecular ; Amino Acid Sequence