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 investigate the protective effect of artemitin against ozone (O₃)-induced pulmonary inflammation in mice and to explore its underlying mechanism. MethodsThirty-two specific pathogen-free (SPF) male C57BL/6 mice were randomly divided into a blank control group, an artemitin-only control group, an O₃-induced model group, and an artemitin-treated group, with 8 mice in each group. In the artemitin-treated group, animals were injected intraperitoneally with artemitin (20 mg/kg) daily, followed by a 1 h exposure to 0.000 25% O₃. The artemitin-only control group received the same artemitin injection but was exposed to clean air. The O₃-induced model group was injected intraperitoneally with an equal volume of normal saline daily, followed by a 1-h exposure to 0.000 25% O₃. The blank control group received the saline injection and was exposed to clean air. All treatments were administered for 7 consecutive days. Pulmonary function indices of the mice in each group were measured using a whole-body plethysmography system. The levels of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) in bronchoalveolar lavage fluid (BALF) were detected by enzyme-linked immunosorbent assay (ELISA). Hematoxylin-eosin (HE) staining was used to observe the histopathological changes in the lung tissue, and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed for untargeted metabolomic analysis to examine the changes in overall metabolic pathways in plasma. ResultsCompared with the O₃-induced model group, artemitin treatment significantly improved O₃-induced lung function impairments (P<0.05), lowered the levels of TNF-α and IL-1β in BALF (P<0.05) and alleviated inflammatory cell infiltration and structural damage in lung tissues. Metabolomics results indicated that artemitin treatment compensatorily enhanced energy supply and oxidized lipid signaling, while inhibiting purine metabolism and tryptophan-derived pathways. ConclusionArtemitin exhibits a significant protective effect against O₃-induced pulmonary inflammation in mice. It corrects upstream metabolic disorders by synergistically modulating tryptophan metabolism, energy metabolism, and lipid inflammatory mediators, thereby inhibiting downstream inflammatory responses and airway hyperresponsiveness. This study not only reveals the pleiotropic mechanism of artemitin in treating airway inflammation but also provides potential metabolic biomarkers and a theoretical basis for its clinical translation.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: In acute and chronic renal inflammatory diseases, the activation of inflammatory cells is involved in the defect of erythropoietin (EPO) production. Ras guanine nucleotide-releasing protein-4 (RasGRP4) promotes renal inflammatory injury in type 2 diabetes mellitus (T2DM). Our study aimed to investigate the role and mechanism of RasGRP4 in the production of renal EPO in diabetes. Methods: The degree of tissue injury was observed by pathological staining. Inflammatory cell infiltration was analyzed by immunohistochemical staining. Serum EPO levels were detected by enzyme-linked immunosorbent assay, and EPO production and renal interstitial fibrosis were analyzed by immunofluorescence. Quantitative real-time polymerase chain reaction and Western blotting were used to detect the expression of key inflammatory factors and the activation of signaling pathways. In vitro, the interaction between peripheral blood mononuclear cells (PBMCs) and C3H10T1/2 cells was investigated via cell coculture experiments. Results: RasGRP4 decreased the expression of hypoxia-inducible factor 2-alpha (HIF2A) via the ubiquitination–proteasome degradation pathway and promoted myofibroblastic transformation by activating critical inflammatory pathways, consequently reducing the production of EPO in T2DM mice. Conclusion RasGRP4 participates in the production of renal EPO in diabetic mice by affecting the secretion of proinflammatory cytokines in PBMCs, degrading HIF2A, and promoting the myofibroblastic transformation of C3H10T1/2 cells.

Objective To analyze the characteristics and risk factors associated with postoperative deep vein thrombosis(DVT)of the lower extremities in patients undergoing surgery for lumbar degenerative diseases.Methods A retrospective analysis was conducted on clinical data from 298 patients who were hospitalized for lumbar degenerative diseases and underwent lumbar spine surgery treatment in the First Affiliated Hospital of Nanchang University from October 1,2022 to April 15,2023.Patients were divided into DVT group(n=71)and non-DVT group(n=227)according to whether DVT of the lower limbs occurred within 1 week postoperatively.The incidence and distribution characteristics of postoperative DVT were analyzed.Univariate and binary logistic regression analyses were performed to identify risk factors for DVT,and receiver operating characteristic(ROC)curves were used to determine cut-off values for relevant risk factors.Results A total of 298 patients were included,among whom 159 were males(53.4%)and 139 were females(46.6%),with an average age of(64.5±9.8)years.DVT occurred in 71 patients,and the incidence of lower extremity DVT was 23.8%.In the DVT group,there were 49 cases(69.0%)of intermuscular vein thrombosis,and 22 cases of other types of thrombosis(7 cases of peroneal vein thrombosis,4 cases of posterior tibial vein thrombosis,3 cases of common femoral vein thrombosis,1 case of anterior tibial vein thrombosis,and 7 cases of multiple thrombosis);58 cases(81.7%)had DVT in one lower extremity,and 13 cases(18.3%)had DVT in both lower extremities.Univariate analysis results showed that age,body mass index(BMI),length of hospital stay,history of hypertension,operative time,and intraoperative blood loss were associated with the occurrence of lower extremity DVT after surgery for lumbar degenerative diseases(P<0.05).Binary logistic regression analysis results indicated that older age(OR=1.079,P<0.01),higher BMI(OR=1.130,P=0.01),history of hypertension(OR=2.992,P<0.01),and larger intraoperative blood loss(OR=1.002,P=0.03)were independent risk factors for the occurrence of lower extremity DVT.ROC curve analysis demonstrated that patients with age>58.5 years,BMI>24.01 kg/m2,history of hypertension,and intraoperative blood loss>550 ml had a significantly increased risk of postoperative lower limb DVT.Conclusions The incidence of lower extremity DVT after surgery for lumbar degenerative disease is high,and intermuscular venous thrombosis is more common.Older age,higher BMI,history of hypertension,and larger intraoperative blood loss are independent risk factors for the occurrence of lower extremity DVT after surgery.

Objective To investigate the effects of peripheral blood neutrophil infiltration on the polarization regulation of cerebral resident microglia under a permanent ischemic stroke model.Methods Fifty-eight C57BL/6 mice were divided into two groups.One group was sham group,and the other group of mice was subjected to permanent middle cerebral artery occlusion surgery.Mice were euthanized 48 hours,7 days,14 days,and 30 days after surgery for tissue collection.Western blotting was used to detect expression levels of M1 microglia markers CD 16,M2 microglia marker arginase 1(Arg1),inflammatory cytokine interleukin-1 β(IL-1β),and neutrophil marker myeloperoxidase(MPO)in brain tissue.Immunofluorescence histochemical staining was used to assess neutrophil infiltration and M2 microglial distribution around the infarct area in brain sections.In vitro,purified neutrophils were co-cultured with BV2 microglial cells.After lipopolysaccharide stimulation,the phagocytosis of neutrophils by BV2 cells was observed,and the expression levels of CD16 and Arg1 proteins in BV2 cells were detected.Results Western blotting showed that the levels of CD16(P＜0.05),IL-1β(P＜0.001),and MPO(P＜0.05)in brain tissue increased significantly 48 hours and 7 days after surgery,then decreased,with MPO expression returning to normal levels 30 days after surgery.Immunofluorescence showed a significant increase of MPO-positive cells around the infarct area of the mouse cerebral cortex 48 hours after surgery(P＜0.001),followed by a decrease(P＜0.05).The number of ionized calcium binding adapter molecule 1(Iba1)and MPO double-positive cells gradually increased after surgery,and reached their peak at 14 days(P＜0.05).Iba1 and Arg1 double-positive cells also increased significantly 7 days(P＜0.05)and 14 days(P＜0.01)after surgery.In vitro,co-culture experiments showed that after BV2 phagocytosing neutrophils,CD 16(P＜0.05)significantly decreased and Arg1 significantly upregulated(P＜0.05).Conclusion In a permanent ischemic stroke model,microglia transition from M1 to M2 type after phagocytosing neutrophils,and the injured brain area changes from pro-inflammatory state to anti-inflammatory state.

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*

Descurainiae Semen Lepidii Semen, also known as Tinglizi, originates from Brassicaceae plants Descurainia sophia or Lepidium apetalum. The former is commonly referred to as "Southern Tinglizi(Descurainiae Semen)", while the latter is known as "Northern Tinglizi(Lepidii Semen)". To scientifically and accurately identify the origin of Tinglizi medicinal materials and traditional Chinese medicine products, this study developed a specific DNA mini-barcode based on chloroplast genome sequences. By combining the DNA mini-barcode with DNA metabarcoding technology, a method for the qualitative and quantitative identification of Tinglizi medicinal materials and Chinese patent medicines was established. In this study, chloroplast genomes of Southern Tinglizi and Northern Tinglizi and seven commonly encountered counterfeit products were downloaded from the GenBank database. Suitable polymorphic regions were identified to differentiate these species, enabling the development of the DNA mini-barcode. Using DNA metabarcoding technology, medicinal material mixtures of Southern and Northern Tinglizi, as well as the most common counterfeit product, Capsella bursa-pastoris seeds, were analyzed to validate the qualitative and quantitative capabilities of the mini-barcode and determine its minimum detection limit. Additionally, the mini-barcode was applied to Chinese patent medicines containing Tinglizi to authenticate their botanical origin. The results showed that the developed mini-barcode(psbB) exhibited high accuracy and specificity, effectively distinguishing between the two authentic origins of Tinglizi and commonly encountered counterfeit products. The analysis of mixtures demonstrated that the mini-barcode had excellent qualitative and quantitative capabilities, accurately identifying the composition of Chinese medicinal materials in mixed samples with varying proportions. Furthermore, the analysis of Chinese patent medicines revealed the presence of the adulterant species(Capsella bursa-pastoris) in addition to the authentic species(Southern and Northern Tinglizi), indicating the occurrence of adulteration in commercially available Tinglizi-containing products. This study developed a method for the qualitative and quantitative identification of multi-origin Chinese medicinal materials and related products, providing a model for research on other multi-origin Chinese medicinal materials.
DNA Barcoding, Taxonomic/methods* ; Drugs, Chinese Herbal/chemistry* ; Drug Contamination ; Genome, Chloroplast ; Medicine, Chinese Traditional

Background: In acute and chronic renal inflammatory diseases, the activation of inflammatory cells is involved in the defect of erythropoietin (EPO) production. Ras guanine nucleotide-releasing protein-4 (RasGRP4) promotes renal inflammatory injury in type 2 diabetes mellitus (T2DM). Our study aimed to investigate the role and mechanism of RasGRP4 in the production of renal EPO in diabetes. Methods: The degree of tissue injury was observed by pathological staining. Inflammatory cell infiltration was analyzed by immunohistochemical staining. Serum EPO levels were detected by enzyme-linked immunosorbent assay, and EPO production and renal interstitial fibrosis were analyzed by immunofluorescence. Quantitative real-time polymerase chain reaction and Western blotting were used to detect the expression of key inflammatory factors and the activation of signaling pathways. In vitro, the interaction between peripheral blood mononuclear cells (PBMCs) and C3H10T1/2 cells was investigated via cell coculture experiments. Results: RasGRP4 decreased the expression of hypoxia-inducible factor 2-alpha (HIF2A) via the ubiquitination–proteasome degradation pathway and promoted myofibroblastic transformation by activating critical inflammatory pathways, consequently reducing the production of EPO in T2DM mice. Conclusion RasGRP4 participates in the production of renal EPO in diabetic mice by affecting the secretion of proinflammatory cytokines in PBMCs, degrading HIF2A, and promoting the myofibroblastic transformation of C3H10T1/2 cells.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.