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.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Objective To investigate disparities in health levels among populations across different regions of China and analyze the relationship between these disparities and regional, social, and economic factors, and to provide recommendations to promote health equity. Methods Based on the data from the Seventh National Population Census, this study employed spatial autocorrelation analysis and the spatial Durbin model to conduct spatial and temporal analyses of the health status of the national population from 2012 to 2021, focusing on the regional distribution of health levels and related influencing factors. Results 1. Regional disparities: The mortality rate in Gansu Province rose from 6.05‰ in 2012 to 8.26‰ in 2021, whereas the mortality rate in eastern provinces such as Hainan Province was relatively low in 2021 (5.39‰). 2. Spatial clustering: The spatial correlation of mortality rates was weak (Moran's I: 0.134-0.245), and the high mortality clusters showed a shift from southwest to northeast region. 3. Influencing factors: Economic conditions, education quality, urbanization levels, and healthcare resources significantly impacted population mortality rates. Conclusion The present study identifies pronounced regional disparities in population health, providing a scientific basis for formulating targeted healthcare policies. Additionally, this study highlights the critical importance of spatial analysis in understanding and addressing public health issues to advance health equity.

Objective To explore the mediating role of activities of daily living (ADL) in pain and depressive symptoms in the elderly in China. Methods Utilizing the data from 2020 China Health and Retirement Longitudinal Study, 4403 Chinese elderly individuals aged ≥ 60 years old were selected as the research subjects. Depression Scale (CES-D 10) of the Center for Epidemiological Survey and ADL scale were used in the study. The PROCESS4.1 macro was used to test the mediating effect of daily living activities between pain and depressive symptoms, and the Bootstrap method was applied for verification of the mediating variables. Results A total of 2368 cases of depressive symptoms were detected in the elderly in China, with a detection rate of 53.78%. Pain was positively correlated with depressive symptoms (r=0.27, P<0.01), and activities of daily living were negatively correlated with pain and depressive symptoms (r=-0.27, -0.337, P<0.01). The results showed that the total effect value of pain on depressive symptoms was 0.33, the direct effect value was 0.24, and the mediating effect value of daily living activities was 0.09, accounting for 27.27%. Conclusion Pain and activities of daily living are important factors influencing depressive symptoms in the elderly, and activities of daily living play a partial mediating role in the relationship between pain and depressive symptoms in the elderly.

Objective To investigate the association between high-density lipoprotein cholesterol(HDL-C)levels and all-cause and cause-specific mortality in the elderly population,and to determine the optimal HDL-C concentration range associated with the lowest mortality risk.Methods This prospective cohort study included 16,632 elderly individuals aged≥60 years from the National Health and Nutrition Examination Survey(NHANES)between 1999 and 2018.Participants were divided into 5 groups based on HDL-C levels:<40 mg/dl group(n=2933),40-49 mg/dl group(n=4602),50-59 mg/dl group(n=3824),60-79 mg/dl group(n=3988),and≥80 mg/dl group(n=1285).Mortality data were obtained by linking records with the National Death Index(NDI).Cox proportional hazards regression and restricted cubic spline curves were used to analyze the association between HDL-C and mortality risk.Subgroup and sensitivity analyses were conducted.Results Multivariable-adjusted Cox models showed that,compared with 50-59 mg/dl HDL-C group,HDL-C<40 mg/dl group had significantly higher risks of all-cause mortality(HR=1.17,95%CI 1.05-1.31,P=0.006),cardiovascular mortality(HR=1.32,95%CI 1.08-1.58,P=0.001),and non-cardiovascular mortality(HR=1.14,95%CI 1.02-1.27,P=0.013).HDL-C≥80 mg/dl group had significantly higher risks of all-cause mortality(HR=1.18,95%CI 1.03-1.35,P=0.027)and non-cardiovascular mortality(HR=1.23,95%CI 1.03-1.46,P=0.029),but no significant effect on cardiovascular mortality(HR=1.07,95%CI 0.88-1.28,P=0.492).Restricted cubic spline curves revealed U-shaped associations between HDL-C and all-cause/non-cardiovascular mortality,and an inverse J-shaped association with cardiovascular mortality.These patterns were consistent in both elderly men and women.The HDL-C levels associated with the lowest risk were 56 mg/dl(95%CI 53-59 mg/dl)for all-cause mortality,54 mg/dl(95%CI 51-57 mg/dl)for cardiovascular mortality,and 59 mg/dl(95%CI 56-62 mg/dl)for non-cardiovascular mortality.Subgroup analyses showed no significant effect modifications.Sensitivity analyses confirmed the primary findings.Conclusions In the elderly population,extremely high HDL-C levels increase the risks of all-cause mortality and non-cardiovascular mortality,but do not elevate cardiovascular mortality.The risk of all-cause mortality is the lowest at an HDL-C concentration of 56 mg/dl.

AIM To investigate the effects of Shuyu Pills combined with evolimus on the epithelial mesenchymal transformation of triple negative breast cancer cells 4T1 and MDA-MB-231 induced by TGF-β1.METHODS The 4T1 and MDA-MB-231 cells were divided into the blank group and the induction group to induce the epithelial mesenchymal transformation with TGF-β1 cytokine treatment,followed by the assignment into the model group,the Shuyu Pills group,the everolimus group and the Shuyu Pills combined with everolimus group.CCK8 method,plate cloning method,cell scratch test and Transewll test were used to detect the proliferation,cloning formation,migration and invasion ability of the cells whose expressions of E-cadherin,N-cadherin,Vimentin,MMP9,MMP2 and pathway proteins PTEN,PI3K,Akt and mTOR were detected by Western blot.RESULTS Compared with the blank group,the induction group displayed a cell morphological change from epithelioid to stromal,decreased expression of E-cadherin protein(P＜0.01);and increased protein expressions of N-cadherin and Vimentin(P＜0.05).Compared with the model group,each group intervened with the medicine displayed decreased proliferation,clone formation,migration and invasion ability of both kinds of cells(P＜0.01);increased protein expressions of PTEN and E-cadherin(P＜0.05,P＜0.01);and decreased protein expressions of PI3K,Akt,mTOR,N-cadherin,Vimentin,MMP9 and MMP2(P＜0.05,P＜0.01);and more significantly in the Shuyu Pills combined with evolimus group(P＜0.05,P＜0.01).CONCLUSION With a more ideal effect than the single uses in inhibiting the TGF-β1-induced epithelial mesenchymal transformation of triple-negative breast cancer cells,the combination use of Shuyu Pills and everolimus may work through the regulation of PI3K/Akt/mTOR signaling pathway.

AIM To establish a UPLC-MS/MS method for the simultaneouscontent determination of notoginsenosides R1,Fc,Fe,ginsenosides Re,Rg1,Rf,F3,Rg2,Ra2,Rb1,Ro,Rc,F1,Ra1,Rb2,Rb3,Rd,F2,Rg5,chikusetsusaponin Ⅳ a,20(S)-ginsenoside Rg3 and 20(R)-ginsenoside Rg3 in Dengzhan Shengmai Capsules.METHODS The analysis was performed on a 35 ℃ thermostatic Accucore Phenyl Hexyl column(2.1 mm×100 mm,2.6 μm),with the mobile phase comprising of 0.1％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and heated electrospray ionization source was adopted in negative ion scanning with parallel reaction monitoring mode.Subsequently,cluster analysis,principal component analysis and partial least squares discriminant analysis were adopted.RESULTS Twenty-two saponins showed good linear relationships within their own ranges(r ≥ 0.996 4),whose average recoveries were 98.5％-101.6％with the RSDs of 1.3％-5.2％.Ten batches of samples were clustered into four types,three principal components demonstrated the accumulative variance contribution rate of 90.265％,ginsenosides Rb1,Rg2,Rb2,Rd,Rg1,Ro,Rf,Re,Rg5,Rc were taken as potential quality markers.CONCLUSION This simple,efficient,accurate and sensitive method can be used for the quality control of Dengzhan Shengmai Capsules.

Alcoholic liver disease(ALD), a major cause of chronic liver disease worldwide, poses a serious threat to human health. Despite the availability of various drugs for treating ALD, their efficacy is often uncertain, necessitating the search for new therapeutic approaches. Traditional Chinese medicine polysaccharides have garnered increasing attention in recent years due to their versatility, high efficiency, and low side effects, and they have demonstrated significant potential in preventing and treating ALD. Emerging studies have suggested that these polysaccharides exert their therapeutic effects through multiple mechanisms, including the inhibition of oxidative stress and the regulation of lipid metabolism, gut microbiota, and programmed cell death. This review summarizes the recent research progress in the pharmacological effects and regulatory mechanisms of traditional Chinese medicine polysaccharides in treating ALD, aiming to provide a scientific basis and theoretical support for their application in the prevention and treatment of ALD.
Humans ; Liver Diseases, Alcoholic/metabolism* ; Polysaccharides/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Oxidative Stress/drug effects* ; Medicine, Chinese Traditional ; Gastrointestinal Microbiome/drug effects* ; Lipid Metabolism/drug effects*