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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective:To explore the CT manifestation characteristics of peripancreatic aneurysms related to pancreatitis (PRPA).Methods:A retrospective analysis was conducted on the clinical data of 15 patients with pancreatitis-related aneurysms collected in Deyang People's Hospital from June 2017 to February 2025. Among them, there were 11 males and 4 females, with an age of (56.5±16.9) years. Record the CT manifestations of the patients, and observe the PRPA tumor-carrying arteries, morphology, quantity, bleeding, calcification, dynamic changes, etc.Results:The CT results showed that there were a total of 26 PRPAs in 15 patients. The aneurysm walls of the PRPAs had no calcification, among which 12 cases were single and 1 case was double. Two cases were multiple. The patients were pancreatitis complicated with muscle fiber dysplasia. Among them, one case had 5 PRPAs and the other had 7 PRPAs. Of the 26 PRPAs, 12 were sac-like, 8 were beaded, 4 were fusiform, and 2 were columnar in shape. The numbers of aneurysm-carrying arteries and PRPA were as follows: 8 in the pancreaticoduodenal artery, 7 in the hepatic artery and its branches, 6 in the splenic artery and its branches, 2 in the left gastric artery, 1 in the gastroduodenal artery, 1 in the middle colonic artery, and 1 in the ileocolonic artery. The CT diagnosis report missed 16 PRPAs, with a missed diagnosis rate of 61.5% (16/26). Among the 11 ruptured PRPAs, 4 were missed, with a missed diagnosis rate of 36.4%. Three missed aneurysms were diagnosed by digital subtraction angiography, and one was retrospectively analyzed by CT. Among the 15 unruptured PRPAs, 12 were missed, with a missed diagnosis rate of 80.0%. All the missed aneurysms were detected by retrospective CT analysis. There were 2 images without re-examination and 24 images with re-examination. Among the PRPAs with re-examination images, 4 disappeared after interventional embolization on the same day, 1 relapsed after interventional embolization on the same day, with a reduction in volume and disappeared after 3 days. Three cases underwent elective interventional embolization, among which two shranked and one enlarged before interventional embolization. Among the 16 cases that did not receive interventional treatment, 7 disappeared on their own, 1 was completely liquefied, 5 shrank but did not disappear, 2 remained stable in size without any change, and 1 increased. Among the re-examined PRPA images, 7 showed signs of enlargement.Conclusion:The main characteristics of PRPA are high rupture rate, prone to missed diagnosis and dynamic changes in size.

This study compared the differences in intestinal absorption characteristics of eleven active components in Rubus multibracteatus(RM) extract(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, epicatechin, catechin, xanthotoxin, p-coumaric acid, caffeic acid, and apigenin-7-O-glucuronide) between normal rats and inflammatory pain model rats using the in-vitro everted intestinal sac model. The RM extract was administered at absorption concentrations of 25.0, 50.0, and 100.0 mg·mL~(-1). The contents of the eleven components in intestinal absorption solution samples were quantified by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS), and their cumulative absorption(Q) and absorption rate constant(K_a) were calculated to evaluate the absorption characteristics of these components in normal rats and inflammatory pain model rats. The results show that except for catechin, epicatechin, and caffeic acid, the cumulative absorption-time curves of the other eight components(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, xanthotoxin, p-coumaric acid, and apigenin-7-O-glucuronide) exhibit an upward trend without saturation, with correlation coefficients(R~2) all > 0.9, indicating linear absorption. However, the overall absorption of all components is not dose-dependent with increasing concentration, suggesting that their absorption mechanisms are not solely passive diffusion. In both normal and model rats, the jejunum shows the highest absorption for all components except xanthotoxin. The overall absorption of seven components(excluding protocatechuic acid, caffeic acid, apigenin-7-O-glucuronide, and luteoloside) in normal rats is better than that in model rats across all intestinal segments. These findings indicate that the pathological state of inflammatory pain alters the intestinal absorption of RM extract, and its mechanism needs further investigation.
Animals ; Rats ; Intestinal Absorption/drug effects* ; Male ; Rats, Sprague-Dawley ; Drugs, Chinese Herbal/metabolism* ; Disease Models, Animal ; Pain/metabolism* ; Intestines/drug effects* ; Intestinal Mucosa/metabolism*

Based on ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology and network pharmacology, this study explored the pharmacodynamic substances and potential mechanisms of Huazhuo Sanjie Chubi Decoction in the treatment of gouty arthritis(GA). UPLC-Q-Exactive Orbitrap-MS technology was used to identify the components in Huazhuo Sanjie Chubi Decoction, and the qualitative analysis of its active ingredients was carried out, with a total of 184 active ingredients identified. A total of 897 active ingredient targets were screened through the PharmMapper database, and 491 GA-related disease targets were obtained from the OMIM, GeneCards, CTD databases. After Venn analysis, 60 intersecting targets were obtained. The component target-GA target network was constructed through the Cytoscape platform, and the STRING database was used to construct a protein-protein interaction network, with 16 core targets screened. The core targets were subjected to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses, and the component-target-pathway network was constructed. It was found that the main active ingredients of the formula for the treatment of GA were phenols, flavonoids, alkaloids, and terpenoids, and the key targets were SRC, MMP3, MMP9, REN, ALB, IGF1R, PPARG, MAPK1, HPRT1, and CASP1. Through GO analysis, it was found that the treatment of GA mainly involved biological processes such as lipid response, bacterial response, and biostimulus response. KEGG analysis showed that the pathways related to the treatment of GA included lipids and atherosclerosis, neutrophil extracellular traps(NETs), IL-17, and so on. In summary, phenols, flavonoids, alkaloids, and terpenoids may be the core pharmacodynamic substances of Huazhuo Sanjie Chubi Decoction in the treatment of GA, and the pharmacodynamic mechanism may be related to SRC, MMP3, MMP9, and other targets, as well as lipids and atherosclerosis, NETs, IL-17, and other pathways.
Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Arthritis, Gouty/metabolism* ; Chromatography, High Pressure Liquid/methods* ; Humans ; Mass Spectrometry/methods* ; Protein Interaction Maps/drug effects*

Objective To investigate the population composition,seasonal dynamics,and infestation levels of cockroaches in Lanzhou,China,and to provide information for the scientific development of cockroach control strategies.Methods Monitoring was conducted at three locations using the sticky trap method.Habitats included farm product markets,catering establishments,hotels,hospitals,and residential areas.Results From 2016 to 2023,the average cockroach density was 0.77 insects per board,with an average infestation rate of 10.84%.Blattella germanica was the dominant species.Seasonal density of cockroaches showed an approximately unimodal distribution,peaking in September.The highest average density and infestation rates were observed in farm product markets.Conclusions Cockroach density and infestation levels in Lanzhou remained relatively low.A comprehensive prevention and control strategy focusing on environmental management in key areas should be implemented according to the seasonal fluctuations.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To evaluate the impact of prophylactic cranial irradiation (PCI) on the prognosis of patients with limited-stage small cell lung cancer (SCLC) in the era of widespread application of MRI.Methods:Clinical data were collected from an open-lable prospective clinical trial on thoracic radiotherapy target volumes for limited-stage SCLC conducted in Sun Yat-sen University Cancer Center and Zhejiang Cancer Hospital between June 2002 and January 2017. In this study, patients who achieved complete remission (CR) or partial remission (PR) after definitive chemoradiotherapy (CRT) were retrospectively analyzed. Stratified analysis was performed according to different clinical efficacies. Patients were divided into different groups according to whether PCI was conducted or not. Survival analysis of patients was carried out. Survival data were calculated by Kaplan-Meier method, and Cox proportional hazards model was applied for multivariate prognostic analysis.Results:Among 309 patients with limited-stage SCLC who received CRT, 133 patients achieved CR and 140 cases obtained PR. These 273 patients were enrolled in this study. Among 133 patients with CR, 29 of them did not receive PCI, and 89 (85.6%) of the remaining 104 patients receiving PCI underwent brain MRI to exclude brain metastasis (BM) before PCI. With a median follow-up time of 22.1 months, the cumulative BM rates were 18.3% and 37.9% in patients who received or did not receive PCI ( P=0.020). The median overall survival (OS) was 30.2 and 30.5 months, and the 1-, 3- and 5-year OS rates were 93.3%, 41.9%, 27.7% and 82.8%, 44.8%, 40.8%, respectively ( P=0.910). Multivariate analysis indicated that baseline Karnofsky performance status (KPS) = 90 was a favorable independent prognostic factor for OS in CR patients ( HR=0.93, 95% CI: 0.89-0.98, P=0.006). Among 140 patients achieving PR, 52 cases did not receive PCI and 80 (90.9%) of the remaining 88 patients received brain MRI before PCI. With a median follow-up time of 18.9 months, the cumulative BM rates were 10.2% and 44.2% ( P<0.001). The median OS was 26.0 and 18.0 months, and the 1-, 3-, and 5-year OS rates were 86.4%, 37.9%, 32.2% and 75.0%, 17.3%, 10.8%, respectively ( P=0.001). Baseline KPS = 90 ( HR=0.93, 95% CI: 0.89-0.97, P=0.001) and PCI ( HR=0.54, 95% CI: 0.36-0.80, P=0.002) were favorable prognostic factors for OS in PR patients. Conclusions:PCI significantly reduces the incidence of BM and prolongs the OS in patients with limited-stage SCLC who achieve PR after CRT, but it fails to significantly prolong the OS of CR patients.