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.

Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective To explore the relationship between levels of vascular endothelial growth factor(VEGF),insulin-like growth factor 1(IGF-1),and transforming growth factor-β1(TGF-β1)in the synovial fluid of children with avascular necrosis of the femoral head(also known as Perthes disease)and the efficacy of postoperative revascularization,aiming to provide a basis for subsequent diagnosis and treatment.Methods A retrospective study was conducted on 262 children with Perthes disease admitted to the Affiliated Hospital of Gansu University of Chinese Medicine from January 2023 to June 2024.Based on postoperative revascularization efficacy,patients were divided into good revascularization group(n=228)and poor revascularization group(n=34).For poor revascularization group,a 1:2 matched case-control design was used to select 68 age-matched children with hip synovitis who underwent hip joint fluid puncture as control group.Additionally,82 children with Perthes disease treated at the hospital from June 2024 to January 2025 were enrolled as a validation cohort for nomogram model verification.The expression levels of VEGF,IGF-1 and TGF-β1 in the synovial fluid of three groups were compared.Confounding biases were controlled through univariate and stratified analyses.Binary logistic regression analysis was used to identify independent factors affecting the revascularization effect.R software was utilized to draw and verify the nomogram model for predicting the postoperative revascularization effect.Results The levels of VEGF,IGF-1 and TGF-β1 in the synovial fluid of children in poor revascularization group were all higher than those in control group and good revascularization group(P<0.05).After three types of reconstructive surgeries,the levels of VEGF,IGF-1 and TGF-β1 in the synovial fluid of children with poor revascularization were all higher than those in children with good revascularization(P<0.05);however,there was no statistically significant difference in the above indicators among different surgical types(P>0.05).Binary logistic regression analysis showed that the levels of VEGF,IGF-1,and TGF-β1 in the synovial fluid were independent risk factors for poor postoperative revascularization in children with Perthes disease.The area under the ROC curve of the nomogram model established accordingly for predicting poor postoperative revascularization in children with Perthes disease was 0.875(95%CI 0.805-0.945),with a sensitivity of 0.874 and a specificity of 0.851.Moreover,the calibration curve and decision curve analysis(DCA)indicated that the model had good clinical applicability.Conclusions The increased levels of VEGF,IGF-1 and TGF-β1 in synovial fluid are associated with poor postoperative revascularization in children with Perthes disease.These three factors are expected to become prognostic indicators for children with Perthes disease.

Objective To explore the effects of pretreatment with rehmannia glutinosa polysaccharide(RGP)and basic fibroblast growth factor(FGF-2)alone or in combination on the differentiation of bone marrow mesenchymal stem cells(BMSCs)into cardiomyoid cells and the related mechanisms.Methods BMSCs pretreated with FGF-2 and RGP alone or in combination were cultured for 1,2,and 4 weeks.The expression levels of NKx2.5 and GATA-4 were detected by Real-time PCR.BMSCs pretreated with FGF-2 or RGP alone and PI3K/Akt signaling pathway inhibitor LY294002 were used to detect the expression levels of myocardial specific proteins and related pathway proteins by Western blotting.Results Compared with the blank control group,pretreatment with FGF-2 or RGP alone increased the expression rate of myocardial specific markers,and the effect of combined pretreatment with FGF-2 and RGP was more obvious(P＜0.05).Compared with pretreatment with FGF-2 or RGP alone,pretreatment with LY294002 combined with FGF-2 or RGP significantly down-regulated the expression of cardiac-specific proteins in BMSCs and inhibited the phosphorylation of Akt(P＜0.05).Conclusion Both FGF-2 and RGP can induce BMSCs to differentiate into cardiomyocyte-like cells by regulating PI3K/Akt signaling pathway,and the combination of FGF-2 and RGP has a better inductive effect.

Objective To analyze the development trends,research status,and hotspots in the field of gut microbiota and cognitive dysfunction from 2015 to 2025,providing a more comprehensive reference for researchers in this field and exploring cutting-edge research directions.Methods Liter-ature in relevant research fields was retrieved from the WoSCC database.Bibliometric and visualiza-tion analyses were conducted using VOSviewer,CiteSpace,ScimangoGraphica,and the R package"bibliometrix".Results A total of 665 eligible English articles were retrieved from the WoSCC data-base.The analysis revealed that the number of publications has shown an increasing trend annually since 2018.China and the United States had the leading roles globally in terms of the number of pub-lications and collaborative relationships.Huazhong University of Science and Technology are the most influential research institution in this field,with NUTRIENTS(IF=4.8)being the journal with the highest number of publications,and KIM D H ranking first in outstanding contributions and influ-ence.Keyword co-occurrence analysis indicated that"neuronal apoptosis","SCFAs","synaptic dysfunction",and"dementia prevention"were the research hotspots in this field.Furthermore,trend analysis revealed that short-chain fatty acids(SCFAs),important metabolites of gut microbiota,insulin resistance,and neuroinflammation were the research frontiers in this field,highlighting insulin resistance as a mediating hub in microbiota-cognition research.Additionally,traditional Chinese medicine interventions(such as electroacupuncture)were identified as an emerging characteristic direction in this field.Conclusion Through bibliometric research,the current status and develop-ment trends of gut microbiota and cognitive dysfunction research are analyzed from multiple perspec-tives in the paper.Comprehensive insights into the cutting-edge dynamics of this research field are provided,offering valuable references for further in-depth research by more scholars in this area.

AIM To study the chemical constituents from the buds of Aralia chinensis L.var.nuda Nakai and their in vitro anti-inflammatory activities.METHODS The 70％ethanol extract from the buds of A.chinensis var.nuda was isolated and purified by silica gel,Sephadex LH-20,ODS and semi-preparative HPLC,then the structures of compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities in vitro were evaluated by RAW264.7 model.RESULTS Sixteen compounds were isolated and identified as 4-(2,2-dibutoxyethyl)phenol(1),trans-linalool-3,7-oxide-6-O-β-D-glucopyranoside(2),2'-O-(9Z,12Z,15Z-octadecatrienoyl)glyceryl β-D-galactopyranoside(3),quercetin-3-O-β-D-glucopyranoside(3'→ O-3''')quercetin-3-O-β-D-galactopyranoside(4),syringaresinol-4'-O-β-D-glucopyranoside(5),p-hydroxybenzaldehyde(6),7α-hydroxystigmasterol 3-O-β-D-glucopyranoside(7),trans-p-hydroxy cinnamic acid methyl ester(8),funingensin A(9),3,4-dihydroxy-acetophenone(10),N-acetyltyramine(11),3,4-di-O-caffeoyl quinic acid(12),chlorogenic acid(13),aralia cerebroside(14),caffeic acid methyl ester(15),tetradecanoic acid(16).The IC50values of compounds 8,10,12 and 13 were(22.19±1.59),(35.25±1.30),(13.38±0.72),(15.73±1.16)μmol/L,respectively.CONCLUSION Compound 1 is a new compound,2-13 are isolated from genus Aralia for the first time.Compounds 8,10,12,13 exhibit significant in vitro anti-inflammatory activities.

Objective:To explore the relationship between serum human epididymal secretory potein 4(HE4),carcinoembryonic antigen(CEA),carbohydrate antigen 199(CA199)levels with clinicopathological characteristics and prognosis of uterine fibroid patients.Methods:87 patients who were diagnosed with uterine fibroids in Xiamen Maternal and Child Health Hospital during the period of January 2019 to January 2024 were selected as the observation group,and 87 women who underwent health checkups during the same period were selected as the control group.The serum HE4,CEA and CA199 levels were detected and compared between the two groups before and 1 month after surgery.The relationship between serum HE4,CEA and CA199 levels before surgery and their clinicopathological characteristics was analyzed.The patients with uterine fibroids were followed up for 1 year after surgery,and they were divided into recurrence group and non-recurrence group according to the recurrence status,influencing factors of postoperative recurrence were analyzed by multiple logistic regression model,the predictive value of CEA,HE4,and CA199 for postoperative recurrence was analyzed by receiver operating characteristic(ROC)curves.Results:Serum HE4,CEA and CA199 levels before surgery in the observation group were significantly higher than those in the control group(P＜0.05).Serum HE4,CEA and CA199 levels before surgery in uterine fibroids patients were significantly correlated with breast hyperplasia,the location of the fibroids,the number of tumors and the maximum diameter of the fibroids(P＜0.05).87 patients with uterine fibroids were followed up for 1 year after surgery,with a recurrence rate of 26.44％.Serum HE4,CEA and CA199 levels at 1 month after surgery in the recurrence group were significantly higher than those in the non-recurrence group(P＜0.05).Elevated HE4,elevated CEA and elevatedCA199 were independent risk factors for postoperative recurrence in uterine fibroids patients(P＜0.05).The area under the curve(AUC)of combined prediction of CEA,HE4,and CA199 for predicting postoperative recurrence of uterine fibroma patients was 0.880.Conclusion:Serum HE4,CEA,and CA199 in uterine fibroids patients are abnormally elevated,and closely related to their clinicopathological characteristics.Serum HE4,CEA,and CA199 levles are influencing factors for postoperative recurrence in uterine fibroids patients and have good predictive value for postoperative recurrence.

Nichrome(NiCr)wire with strong mechanical properties,excellent flexibility,good corrosion resistance and high thermal stability was selected as a promising fiber substrate to replace the fragile fused-silica counterpart.The NiCr layered double hydroxide nanosheets(NiCr LDHs NSs)were in-situ grown on the NiCr fiber substrate.Then,the extraction performance of the NiCr@NiCr LDHs NSs fiber was evaluated using four kinds of chlorophenol(CPs)as model compounds combined with high performance liquid chromatography-ultraviolet detection(HPLC-UV).The results showed that the assembled fibers exhibited superior extraction selectivity and enhanced extraction efficiency for CPs in comparison to commercial PA,PDMS,and PDMS/DVB fibers.Under the optimized experimental conditions,the established method showed good linearity in the range of 0.2-200 μg/L,with coefficient of determination(R2)>0.9989.The limits of detection(LODs)and limits of quantification(LOQs)were 0.050-0.157 μg/L and 0.165-0.502 μg/L,respectively.Relative standard deviations(RSDs)for intra-day and inter-day analyses ranged from 2.85%to 4.05%and from 3.16%to 4.96%,respectively.The developed method with the constructed fiber was applied to preconcentration and detection of different types of CPs in real water samples,showing satisfactory recoveries ranging from 80.0%to 106.9%,with RSDs of 3.12%-7.81%.Moreover,the NiCr@NiCr LDHs NSs fiber could maintain good extraction performance even after 240 extraction-desorption cycles.

Time-of-flight secondary ion mass spectrometer(TOF-SIMS)is a highly sensitive surface analysis instrument with high spatial resolution.Traditional TOF-SIMS instruments for sample targets use high field extraction methods.Although the ion collection efficiency is high,it is prone to issues such as low-energy ion beam defocusing,sample morphology sensitivity,and organic molecule ion dissociation.This study aimed to develope an efficient low-field ion extraction system suitable for TOF-SIMS with a continuous beam source.The SIMION simulation software was used to construct a model of the secondary ion optical extraction system.The key factors affecting the extraction efficiency were studied,and the structural parameters of the extraction cone were optimized.Using an indium target as the sample,an experimental test of the performance of the ion extraction system was carried out on the TOF-SIMS instrument.The influences of the voltages of the ion extraction cone and the single lens on the ion extraction efficiency were consistent with the simulation results.By adopting the technology of deflection and coaxial dynamic compensation,the imaging field of view of the ion extraction system was increased to 500 μm×500 μm.The energy window of the ion extraction system reached 10 eV,and the large imaging depth of field of 400 μm was achieved.In the test of a 5 mg/L cholesterol thin film sample,the signal-to-noise ratio of the characteristic peak[M-OH]+reached 4453.The results showed that this low-field secondary ion extraction system effectively improved the performance of the continuous beam TOF-SIMS instrument.