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.

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective:To investigate the the differential expression of EIF5A2 in intrahepatic cholangiocarcinoma cell lines RBE,HCCC9810,and HUCCT1,and its effects on HCCC9810 cell migration and invasion,epithelial mesenchymal transition,and PI3K/AKT signaling pathway.Methods:The differential expression of EIF5A2 in RBE,HCCC9810,and HUCCT1 cell lines was detected using WB method.The HCCC9810 cell line,with the highest expression of EIF5A2,was selected for this experiment.The expression of EIF5A2 in HCCC9810 cell line was silenced by transient transfection of small interfering RNA.The best silencing effect of small interfering RNA was screened by WB.Scratch assay and Tran-swell migration invasion assay were used to detect the effect of silencing EIF5A2 on the migration and invasion ability of HCCC9810 cells.WB was used to detect the effect of silencing EIF5A2 on PI3K/AKT signaling pathway and epithelial mesenchymal transition in HCCC9810 cells.Results:The WB results showed that EIF5A2 had the highest expression in the HCCC9810 cell line,and siRNA1 had the best silencing effect on EIF5A2 in the HCCC9810 cell line.Scratch assay and Transwell migration invasion assay results showed that silencing EIF5A2 in the HCCC9810 cell line resulted in a decrease in cell invasion and metastasis ability(P＜0.05).At the same time,the expression of p-PI3K and p-AKT in the PI3K/AKT signaling pathway was significantly decreased(P＜0.05),while the epithelial cell marker E-cadherin expression increased(P＜0.05)and the stromal cell marker N-cadherin expression decreased(P＜0.05).Conclusion:EIF5A2 may promote epi-thelial mesenchymal transition and enhance the migration and invasion ability of intrahepatic cholangiocarcinoma cells through the PI3K/AKT signaling pathway.

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 summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Aim To establish and evaluate an alternative meth-od for detecting skin sensitization of cosmetics based on local lymph node assay using bromodeoxyuridine(BrdU)with flow cytometry(FCM).Methods(1)25％hexyl cinnamic alde-hyde(HCA)was chosen as a positive control with an acetone:olive oil(4∶1,V/V,AOO)mixture as a vehicle control for the experiment.The dorsal sides of both ears of mice were treated with test solutions on day 1,day 2,and day 3.Brdu solution was injected inter-peritoneally on day 5.On day 6,the bilateral ears and mandibular lymph nodes were excised,and the number of Brdu positive cells was measured by flow cytometry.The stim-ulation index(SI)was calculated to identify whether it was ≥3,in order to establish the method of LLNA:Brdu-FCM.(2)BrdU-FCM test was conducted using a blind method with the fif-teen reference substances listed in OECD TG429 whose skin sensitization potentials were known.The test substances were dissolved in AOO,N,N-dimethylformamide(DMF)or dimeth-yl sulfoxide(DMSO)at three different concentrations.Tests were performed the same as above.SI and EC2.7 were calculat-ed to evaluate whether the test substance was categorized as a skin sensitizer.The reliability and accuracy of the method were validated by comparing the classification of test substances with that in OECD TG429.Results The SI for 25％HCA was 3.9,showing positive in the skin sensitization test.It demonstrated that the LLNA:Brdu-FCM test method was properly implemen-ted.Nine test substances(2,4-dinitrochlorobenzene,4-pheny-lenediamine,cobalt chloride,2-mercaptobenzothiazole,hexyl-cinnamaldehyde,eugenol,phenyl benzoate,cinnamic alcohol,imidazolidinyl urea)were positive,and six test substances(methyl methacrylate,chlorobenzene,isopropanol,lactic acid,methyl salicylate,salicylic acid)were negative.The method was evaluated with sensitivity of 90％,specificity of 100％,positive prediction rate of 100％,negative prediction rate of 83％,false positive rate of 0％,false negative rate of 17％and accuracy of 93％.The LLNA:BrdU-FCM assay could correctly categorize the test substances that were skin sensitizers or non-sensitizers.Conclusion The LLNA:BrdU-FCM assay appears to be a relia-ble predictor of skin sensitization protential of chemicals,and it is expected to an alternative method for identifying skin sensitization as a supplementary in safety evaluation of cosmetic ingredient.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.