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.

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size

Objective To investigate the effect of nursing intervention based on Kano model on surgical outcomes and negative emotions in patients with diabetic retinopathy(DR).Methods A to-tal of 262 DR patients who underwent vitrectomy were randomly divided into control group(n=131)and observation group(n=131)using the random number table method.The control group received routine nursing intervention,while the observation group received nursing intervention based on the Kano model.The surgical outcomes[visual acuity level and improvement in visual function],nursing needs[Perioperative Nursing Needs Questionnaire for DR Surgical Patients],negative emotions[Self-Rat-ing Anxiety Scale(SAS)and Self-Rating Depression Scale(SDS)],and quality of life[Chinese Version of Low Vision Quality of Life Scale(CLVQOL)]were compared between the two groups.The incidence of complications during the intervention period was also recorded in both groups.Results After interven-tion,the visual acuity levels in both groups increased compared to those before intervention,and the rates of low vision decreased(P＜0.001).The visual acuity level in the observation group was high-er,and the rate of low vision was lower than that in the control group,with statistically significant differences(P＜0.001).After intervention,the scores for peripheral visual field,sensory adaptation,and limitations in daily activities decreased in both groups compared to those before intervention,and the scores in the observation group were lower than those in the control group(P＜0.05).After intervention,the SAS and SDS scores in both groups decreased compared to those before interven-tion,and the scores in the observation group were lower than those in the control group(P＜0.001).After intervention,the CLVQOL scores in both groups increased compared to those before the intervention,and the score in the observation group was higher than that in the control group(P＜0.001).The incidence of complications in the control group was higher than that in the obser-vation group(12.22％versus 4.58％,P＜0.05).Conclusion Nursing intervention based on the Kano model can improve visual acuity level,visual function,and negative emotions,enhance the quality of life,and further reduce the incidence of complications in DR patients undergoing vitrectomy.

Objective To explore the economic toxicity perceptions and experiences of caregivers of colorectal cancer patients from the perspective of social ecological systems theory.Methods Using purposive sampling,18 caregivers of colorectal cancer patients hospitalized in the gastrointestinal sur-gery and oncology departments of a tertiary grade A hospital in Yangzhou were selected for semi-struc-tured interviews.The Colaizzi 7-step analysis method was employed to organize and analyze the data.Results Three main themes and nine sub-themes were extracted regarding the economic toxicity expe-riences and needs of caregivers of colorectal cancer patients.Microsystem included multiple negative experiences,impacted individual health,and difficulties in surrogate decision-making;mesosystem in-cluded heavy family financial burden,altered family lifestyle,and tense family atmosphere;macrosys-tem included needs for the scope and intensity of medical insurance reimbursement,needs for medical resources and services,and a desire for social support.Conclusion The ecological system of caregiv-ers of colorectal cancer patients is not optimistic,and is generally affected by economic toxicity.Inter-vention strategies can be sought from multiple aspects,including alleviating the negative experiences of caregivers,strengthening social support,and paying attention to the needs of caregivers,aiming to re-duce the level of economic toxicity among caregivers of colorectal cancer patients.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

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

AIM To optimize the cellulase-assisted ultrasound extraction process for total flavonoids from Plumbago zeylanica L.,and to evaluate their anti-oxidant activity.METHODS With extraction time,liquid-solid ratio,cellulase addition amount,extraction temperature and ultrasonic power as influencing factors,extraction rate of total flavonoids as an evaluation index,the extraction process was optimized by response surface method on the basis of single factor test.Subsequently,The scavenging rates of extract on DPPH,ABTS and OH free radicals were determined.RESULTS The optimal conditions were determined to be 34∶1 for liquid-solid ratio,3％for cellulase addition amount,51 ℃ for extraction temperature,38 min for extraction time,and 400 W for ultrasonic power,the extraction rate of total flavonoids was(33.411±0.97)％.The IC50 values of three free radicals were 0.13,0.042,3.29 mg/mL,respectively.CONCLUSION This reasonable and reliable method can be used for the cellulase-assisted ultrasound extraction of total flavonoids from P.zeylanica with strong anti-oxidant activity.

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.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Aim To elucidate whether Astragaloside Ⅳcould ameliorate pathological myocardial hypertrophy and fibrosis via the EGR1-SIRT1-PPARα-SCAD signa-ling pathway in TAC mice.Methods After randomi-zing mice into groups,the Sham+AS-Ⅳ group and TAC+AS-Ⅳ group were intragastrically administered 20 mg·kg-1AS-Ⅳ once daily,whereas the Sham+NS group and TAC+NS group were given equivalent saline.Six weeks post-surgery,an evaluation of cardiac function was conducted,heart weight index was compu-ted,morphological alterations in heart were noted,vari-ations in collagen and myocardial hypertrophy indexes were analyzed,ATP content,free fatty acid content,hydroxyproline content,SCAD expression,and enzyme activity were measured,and an initial investigation into the protein expression of EGR1-SIRT1-PPARα-SCAD in myocardial tissues was undertaken.Results After AS-Ⅳ intervention,the heart weight index of TAC mice decreased(P＜0.01),LVAWd,LVAWs,LVPWd and LVPWs values decreased(P＜0.01,P＜0.05),EF％and FS％values increased(all P＜0.01),myocardial hypertrophy markers and collagen area decreased,FFA content,HYP content and collagen expression de-creased(all P＜0.01),SCAD enzyme activity and ex-pression increased(P＜0.01,P＜0.05),and ATP content increased(P＜0.01).The expression of EGR1 protein decreased,and the expression of SIRT1 and PPARα protein increased(all P＜0.01).Conclu-sions AS-Ⅳ may improve fatty acid oxidation via the EGR1-SIRT1-PPARα-SCAD signaling pathway,thereby ameliorating pathological myocardial hypertrophy and fibrosis in TAC model mice.