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.

Objective To explore the anesthetic effects of remimazolam combined with nalbuphine on patients undergoing painless gastroscopy.Methods A total of 120 patients who underwent painless gastroscopy in Handan Central Hospital from August 2019 to May 2021 were selected and randomly divided into the observation group(60 cases)and the control group(60 cases).Patients in the observation group anesthetized with remimazolam and nalbuphine,while patients in the control group anesthetized with propofol and alfentanil.The vital signs,sedative effect,recovery quality,cognitive function,and adverse reactions of patients were compared between the two groups.Results The heart rate(HR),respiratory rate(RR),and the level of pulse oxygen saturation(SpO2)of patients in the observation group were significantly higher than those in the control group(P<0.05).Five minutes after the examination,the Narcotrend score of patients in the observation group was significantly lower than that in the control group(P<0.05).The time from the last administration to awakening,the time from gastroscopy removal to awakening,and the time of leaving the anesthesia recovery room of patients in the observation group were significantly shorter than those in the control group(P<0.05).Five minutes after the examination,the score of the mini-mental state examination(MMSE)of patients in the observation group was significantly higher than that in the control group(P<0.05),while the incidence of adverse reactions was significantly lower than that in the control group(P<0.05).Conclusion The application of remimazolam combined with nalbuphine anesthesia in painless gastroscopy has a relatively ideal effect,which can effectively reduce the adverse reactions of patients and promote rapid postoperative recovery.

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 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.

This study aims to evaluate the efficacy of Chinese medicine injections in treating transient ischemic attack(TIA) based on network Meta-analysis. Randomized controlled trial(RCT) about Chinese medicine injections in treating TIA were retrieved from PubMed, Web of Science, Cochrane Library, EMbase, CNKI, VIP, Wanfang, and SinoMed with the time interval from inception to March 1, 2024. The methodological quality of the included articles was assessed by ROB 2.0, and the GRADE system was employed to evaluate the quality of evidence. The gemtc package of R 4.1.2 was used to perform the network Meta-analysis. Finally, 63 RCTs with a total sample size of 5 750 cases were included, involving 11 Chinese medicine injections(Shuxuetong Injection, Danhong Injection, Shuxuening Injection, Ginkgo Damo Injection, Shenxiong Glucose Injection, Ligustrazine Injection, Salviae Miltiorrhizae and Ligustrazine Hydrochloride Injection, Salvianolic Acids for Injection, Dengzhan Xixin Injection, Guhong Injection, and Xueshuantong Injection). All patients received conventional western medicine treatment, and the experimental group was additionally treated with Chinese medicine injection. Network Meta-analysis yielded the following results.(1) In terms of improving the clinical total response rate, 11 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Dengzhan Xixin Injection + conventional western medicine had the best effect.(2) In terms of reducing plasma viscosity, 7 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Shenxiong Glucose Injection + conventional western medicine had the best effect.(3) In terms of reducing whole blood high shear viscosity, 6 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Guhong Injection + conventional western medicine had the best effect.(4) In terms of reducing whole blood low shear viscosity, 6 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Shuxuening Injection + conventional western medicine had the best effect.(5) In terms of reducing fibrinogen, 9 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Ginkgo Damo Injection + conventional western medicine had the best effect.(6) In terms of increasing the average blood flow velocity, 3 Chinese medicine injections combined with conventional western medicine outperformed conventional western medicine alone, and Shuxuening Injection + conventional western medicine had the best effect. In summary, compared with conventional western medicine alone, Chinese medicine injections combined with conventional western medicine were effective in improving the clinical total response rate and the average blood flow velocity, as well as reducing plasma viscosity, whole blood high shear viscosity, whole blood low shear viscosity, and fibrinogen. However, due to the limited quality and quantity of the included articles, the above conclusions need to be verified by more high-quality, multi-center, and large-sample RCT.
Humans ; Drugs, Chinese Herbal/administration & dosage* ; Injections ; Ischemic Attack, Transient/drug therapy* ; Randomized Controlled Trials as Topic ; Treatment Outcome

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

To improve the quality evaluation system of Hibisci Mutabilis Folium, this study established high performance liquid chromatography(HPLC) fingerprints of Hibisci Mutabilis Folium and evaluated the quality differences of medicinal materials from different places of production by chemometrics. Furthermore, a content measurement method of differential components was established based on quantitative analysis of multi-components by single-marker(QAMS). The fingerprints of 17 batches of Hibisci Mutabilis Folium from different places of production were constructed, with a total of 19 common peaks marked and seven components confirmed. The similarity between the sample fingerprints and the reference fingerprints ranged from 0.890 to 0.974. By utilizing principal component analysis(PCA), hierarchical cluster analysis(HCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA), the chemical patterns of fingerprints were identified. Five components that could be used to evaluate the quality differences of Hibisci Mutabilis Folium were screened, namely peak 6(quercetin 3-O-β-robinobioside), peak 7(rutin), peak 9(kaempferol-3-O-β-robinobioside), peak 10(kaempferol-3-O-rutinoside), and peak 14(tiliroside). The relative correction factors of isoquercitrin, kaempferol-3-O-β-robinobioside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside, and tiliroside were measured with rutin as the internal reference. The QAMS method was established for the content measurement of six flavonoids, and the results showed there was no significant difference compared to the results obtained by an external standard method. In summary, the HPLC fingerprints and QAMS method established in the study, demonstrating stability and accuracy, can provide a reference for the overall quality evaluation of Hibisci Mutabilis Folium.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Principal Component Analysis