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.

Objective To evaluate the efficacy and safety of a novel intravascular lithotripsy(IVL)balloon—Vesscrack shockwave balloon—for vascular preparation before stent implantation in patients with severe coronary artery calcification(CAC).Methods This was a prospective,single-arm,multicenter study conducted in China from June 2022 to October 2022.Patients with severe CAC were treated with the Vesscrack shockwave balloon for lesion preparation,followed by drug-eluting stent(DES)implantation.Of these,33 patients underwent optical coherence tomography(OCT).The primary endpoint was procedural success,defined as successful stent implantation with residual stenosis≤30％and the absence of in-hospital major adverse events,including cardiac death,target vessel-related myocardial infarction,or target lesion revascularization.Results A total of 170 patients[mean age:(65.9±7.9)years,116 males]were enrolled.After treatment with IVL and DES,the minimum lumen diameter increased significantly compared to baseline[(2.34±0.40)mm vs.(0.95±0.33)mm,P＜0.001],the degree of stenosis was significantly reduced[(13.24±6.60)％vs.(65.18±10.59)％,P＜0.001].Procedural success was achieved in 100％of cases,and device success was 98.8％.The 30-day patient-related cardiovascular clinical composite endpoint(POCE)rate was 0.0,with no target lesion failure,no confirmed or potential thrombotic events were observed.The shockwave energy generator demonstrated excellent stability and ease of use.Among the 33 patients assessed with OCT,after IVL intervention,the maximum calcified area of the lumen[(3.51±1.51)mm2 vs.(2.85±1.80)mm2,P＜0.001],and the minimum lumen area within the target lesion[(3.08±1.04)mm2 vs.(2.02±0.75)mm2,P＜0.001],and after DES intervention,the luminal area of the largest calcified site[(6.59±1.64)mm2 vs.(2.85±1.80)mm2,P＜0.001]and the minimum luminal area within the target lesion[(6.19±1.45)mm2 vs.(2.02±0.75)mm2,P＜0.001]were significantly increased,and the differences were statistically significant.Conclusions The Vesscrack shockwave balloon is effective and safe for vascular preparation in patients with severe CAC prior to stent implantation.It achieves significant calcified plaque modification,high procedural success rates,and minimal complications.

Objective To investigate the effect of intravenous injection of remifentanil on the comfort level of birth-giving women with scarred uterus undergoing cesarean section.Methods A total of 82 birth-giving women with scarred uterus who underwent cesarean section in the Suqian Hospital of Jiangsu Provincial People's Hospital from October 2021 to September 2023 were selected and randomly divided into the observation group and the control group(with 41 cases in each group).Before skin resection of cesarean section,the birth-giving women of the observation group were injected with 0.05 μg·kg-1·min-1 remifentanil intravenously until the end of the operation,and these in the control group was injected with the same amount of normal saline.The vital signs and pain at different time points,traction reaction and occurrence of maternal and infant complications were compared between the two groups.Results The mean arterial pressure(MAP),heart rate(HR),pain visual analogue scale(VAS)score and incidence of traction reactions at abdominal exploration in the observation group were significantly lower than those in the control group(P<0.05).There was no significant difference in the neonatal umbilical vein pH value,umbilical vein blood pulse oximetry saturation(SPO2),Apgar scores 1 minute and 5 minutes after birth of newborn,or nausea,vomiting and respiratory depression of birth-giving women between the two groups(P>0.05).Conclusion For birth-giving women with scar uterus who underwent cesarean section,intravenous injection of 0.05 μg·kg-1·min-1 of reifentanil can reduce the fluctuation of vital signs,significantly relieve the traction reaction at abdominal exploration,with a few maternal and infant complications,which is conducive to improving the comfort level of the birth-giving women.

CRY1/2,as important transcriptional regulators of the circadian clock system,play a crucial role in maintaining metabolic homeostasis in body.This article analyzes existing re-search and focuses on the regulatory mechanisms of CRY1/2 in glucose and lipid metabolism.CRY1 plays an important regula-tory role in gluconeogenesis,glycolysis and insulin secretion,maintaining blood glucose homeostasis by regulating the expres-sion of key enzymes.Additionally,CRY1/2 regulate lipid me-tabolism by modulating fat synthesis and absorption,thereby af-fecting energy balance in the body.This article also discuss the potential applications of CRY1/2 in obesity and xenobiotic de-toxification,highlighting their potential value as therapeutic tar-gets.As core factors in metabolic regulation,CRY1/2 have sig-nificant clinical application prospects,and a deeper understand-ing of their molecular mechanisms in glucose and lipid metabo-lism will provide new ideas and strategies for the treatment of chronic metabolic diseases.

Icaritin(ICT)is an 8-isopentenylflavonoid,which is the main effective component of the tra-ditional Chinese medicine Epimedium.Previously,we found that Icaritin inhibits the growth of glioblasto-ma(GBM)cells.Herein we aim to study the in vivo anti-GBM effectiveness of Icaritin and explore its mechanism.The results of MTT assay,flow cytometry,comet assay and cellular immunofluorescence as-say in vitro showed that ICT inhibited the proliferation of four kinds of GBM cells,U87,U251,U118 and A172,induced early apoptosis(P＜0.001)and late apoptosis(P＜0.05)in U87 cells,induced DNA damage in U87 cells,and blocked the growth of U87 cells at the G0/G1 phase(P＜0.0001)in a concen-tration-time-dependent manner.In vivo subcutaneous tumor transplantation tumor experiments showed that feeding 200 mg/kg(P＜0.01)and 400 mg/kg(P＜0.001)ICT had a significant inhibitory effect on the growth of GBM subcutaneous tumors,and had no significant toxic effects on heart,liver,spleen,lung and kidney tissues.The results of network pharmacological analysis,molecular docking and cellular thermodynamic experiments showed that there were 26 possible target proteins between ICT and GBM,a-mong which the expression of p53 in GBM tissues was significantly(P＜0.001)higher than in normal tis-sues,and the binding energy of ICT and p53 was lower;cellular thermodynamic experiments verified that ICT significantly enriched the level of p53 in the living cells of GBM,which indicated that ICT could tar-get p53.The expression of key proteins in the DNA damage repair and apoptosis-associated FOXO signa-ling pathway was detected by ICT.The results showed that the expression of ATR(P＜0.01),P53(P＜0.001),P21(P＜0.05)and γ-H2AX(P＜0.05)was up-regulated,whereas the expression of Cyc-lin E1(P＜0.01),E2F1(P＜0.05),CDK2(P＜0.01),Rb(P＜0.001),p-Rb(P＜0.0001)and WRN(P＜0.0001)expression were down-regulated.There was no significant change in the expres-sion of FOXO 1 in the FOXO pathway or a significant down-regulation of its phosphorylation level.This study demonstrated that ICT could effectively inhibit the growth of GBM cells in vivo.It targets p53 to regulate the DNA damage repair pathway and FOXO signaling pathway to induce GBM cell cycle arrest and apoptosis.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

Objective:This study aimed to evaluate the application effect of opioid-free anesthesia(OFA)in modified radical mastectomy for breast cancer.Methods:80 patients undergoing unilateral modified radical mastec-tomy were randomly divided into two groups:general anesthesia group(G group)and OFA group(O group).The G group received general anesthesia with opioid drugs and a laryngeal mask,while the O group received general anes-thesia with intravenous lidocaine combined with thoracic paravertebral nerve block and a laryngeal mask.The average arterial pressure(MAP)and heart rate(HR)of the patients were recorded at the time of admission(T0),induction(T1),start of surgery(T2),gland resection(T3),and admission to the recovery room(T4).The surgical time,awakening time,ex-tubation time,and getting out of bed time were recorded.The VAS score at 2 hours(T5),6 hours(T6),and 12 hours(T7)after surgery,as well as the systemic immune-inflammatory index(SII)before surgery(T8),6 hours after surgery(T9),and 12 hours after surgery(T10)were recorded.The occurrence of postoperative nausea and vomiting(PONV)and post-mastectomy pain syndrome(PMPS)were recorded.The occurrence of adverse events such as poor nerve block effect,pneumothorax,hematoma,and local anesthetic toxicity were also recorded.Results:The MAP and HR of the O group were more stable than those of the G group during surgery(P＜0.05).The awakening time,extubation time,and getting out of bed time in the O group were earlier than those in the G group(P＜0.05).The VAS and SII values after surgery were significantly lower in the O group than in the G group(P＜0.05).The incidence of PONV was also signifi-cantly decreased(P＜0.05).In addition,no adverse events such as pneumothorax,hematoma,or local anesthetic toxic-ity occurred in the O group.Conclusion:Pioid-free anesthesia is safe and effective in modified radical mastectomy for breast cancer,shortening recovery time,time to first flatus,and time to ambulation,while alleviating postoperative pain,systemic inflammatory response,perioperative hemodynamic fluctuations,and the incidence of postoperative nau-sea and vomiting.

Traditional mass measurement methods are not applicable in microgravity environments,and the main challenge for in-orbit body mass measurement technology based on inertial principles is to address the random errors brought about by the weightless environment.These include additional torques due to shifts in the center of mass,nonlinear accelerations due to non-rigid human bodies,mechanical energy consumption due to organ vibrations,and random vibrations of the measurement device itself.To address the above difficulties,the project proposes a technical scheme based on the principle of linear acceleration,designs and constructs a ground-specific air-floating experimental and simulation platform,studies key data such as motion trajectory,acceleration change,and vibration frequency amplitude during the mass measurement process,and simulates the changes in the center of mass and random vibrations of the human body in a weightless environment.The project has designed an adjustable posture bracket to adapt to changes in the center of mass,enhance body restraint,and greatly reduce shaking;it has also developed an integrated four-bar linkage motion guidance mechanism,high-precision integrated photoelectric distance measurement,and modular motion constant force measurement device to ensure the accurate measurement of acceleration and constant force data.The product has undergone simulation calculations,ground human applicability tests,and in-orbit applicability verification in the space station.Ground test results show that the device achieves a body mass measurement accuracy better than 0.5%,and the dispersion is better than 0.38%;after flight mission verification and evaluation,the in-orbit body mass measurement dispersion is less than 0.4%,which is superior to the SLAMMD,a mass measurement device of the same principle on the International Space Station,and is at the forefront internationally,achieving accurate body mass measurement.