BACKGROUND/OBJECTIVES: Unhealthy diets are associated with chronic stress. An allostatic load (AL) is a comprehensive physiological index that measures the chronic stress response of the body. As a healthy dietary pattern, the role of Dietary Approaches to Stop Hypertension (DASH) in the development of an AL during pregnancy remains unclear. This study examined the relationship between the DASH dietary pattern and AL during pregnancy. SUBJECTS/METHODS: This study was a prospective study. From January 2024 to November 2024, 134 pregnant women in the second trimester (23–27 weeks) and third trimester (32–36 weeks) of pregnancy who met the inclusion and exclusion criteria in the obstetrics clinic of a tertiary general hospital in Anhui Province were selected for a questionnaire survey, physical examination, and laboratory examination. The DASH dietary pattern was assessed using the DASH scoring tool, and the total AL score was calculated using nine biomarkers that represent the cardiovascular, metabolic, and immune systems. Logistic regression was used to analyze the relationship between the DASH score and AL. RESULTS: One hundred and thirty-four pregnant women were included in this study. At the second and third trimesters, 41.8% and 37% of pregnant women, respectively, were in the high AL group. The binary logistic regression results showed that the DASH score was negatively correlated with the AL in the unadjusted model (odds ratio [OR], 0.878; 95% confidence interval [CI], 0.807–0.957; P = 0.003) and adjusted model (OR, 0.878; 95% CI, 0.792–0.973; P = 0.013) in the second trimester, the DASH score was negatively correlated with the AL in the unadjusted model (OR, 0.832; 95% CI, 0.758–0.913; P < 0.001) and adjusted model (OR, 0.806; 95% CI, 0.716–0.908; P < 0.001) in the third trimester. CONCLUSION The DASH score was negatively correlated with the AL in pregnant women.A low DASH score may increase the risk of a high AL, which may have adverse effects on physical and mental health.

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 investigate the effect of epifriedelanol(Epi)on gene expression of P-glycoprotein(P-gp)in human colorectal adenocarcinoma cell line LS174T and its mechanism.Methods LS174T cells were divided into control group and experimental-L,-M,-H groups.Experimental-L,-M,-H groups were treated with 5,10,20 μmol·L-1 Epi,respectively.Control group was treated with 0.1％dimethyl sulfoxide.Polymerase chain reaction was used to detect the mRNA expression level of P-gp.Theeffect of Epi on multidrug resistance protein 1(MDR1/P-gp)luciferase activity was investigated by pregnane X receptor(PXR)-MDR1/P-gp dual luciferase reporter gene assay.In addition,Western Blot was used to detect the protein expression level of P-gp and the nuclear factor-κB(NF-κB)pathway related proteins.Results The relative expression levels of P-gp mRNA in experimental-M,-H groups and control group were 52.24±5.19,23.00±3.52 and 100.00±9.00;the relative expression levels of P-gp protein were 86.37±9.96,74.85±15.92 and 100.00±12.91;the relative activities P-gp luciferase were 230.19±41.32,203.10±52.84 and 279.67±19.20;the relative expression levels of p65(RelA/p65)in nucleus were 132.36±23.93,145.96±25.15 and 100.00±10.88;the relative expression levels of phosphorylation NF-κB inhibits protein kinase α/β(p-IKKα/β)in cytoplasm were 184.00±54.82,290.10±49.59 and 100.00±15.34;the relative expression levels of phosphorylated NF-κB inhibitory protein α(p-IκBα)in cytoplasm were 125.73±18.77,133.69±20.25 and 100.00±8.12;the relative expression levels of IκBα in cytoplasm were 78.36±14.83,70.44±14.57 and 100.00±22.82,respectively.The above indexes of experimental-M and experimental-H groups were compared with control group,and the differences were statistically significant(P＜0.05,P＜0.01,P＜0.001).Conclusion Epi can down-regulate the gene expression of P-gp in human colorectal adenocarcinoma cell line LS174T,and the mechanism may be related to activation of NF-κB and suppression of PXR.

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.

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 investigate the cognition of both doctors and patients in the neurology department of a tertiary hospital in Linyi City regarding graded diagnosis and treatment,and to explore its influencing factors.Methods Convenient sampling was used to select 456 survey subjects from 20 medical institutions within the medical consortium of a tertiary hospital in Linyi City,including 191 medical staffs and 265 patients;A total of 35 medical staffs and patients were interviewed.Results The overall awareness of graded diagnosis and treatment among medical staff and patients was(3.731±0.563)points and(3.136±0.367)points,respectively.The influencing factors of medical staff on grading diagnosis and treatment are professional title,years of work experience,and hospital grade.The influencing factors of patient on grading diagnosis and treatment are age,type of medical insurance,average monthly income,and whether they had a transfer experience.Conclusion While strengthe-ning information technology construction,government supervision and investment,and enhancing the capacity building of grassro-ots medical institutions,it is also necessary to actively promote the construction of close medical consortia to accelerate the forma-tion of a standardized and orderly new system for medical treatment and diagnosis.

Objective To investigate the effect of epifriedelanol(Epi)on gene expression of P-glycoprotein(P-gp)in human colorectal adenocarcinoma cell line LS174T and its mechanism.Methods LS174T cells were divided into control group and experimental-L,-M,-H groups.Experimental-L,-M,-H groups were treated with 5,10,20 μmol·L-1 Epi,respectively.Control group was treated with 0.1％dimethyl sulfoxide.Polymerase chain reaction was used to detect the mRNA expression level of P-gp.Theeffect of Epi on multidrug resistance protein 1(MDR1/P-gp)luciferase activity was investigated by pregnane X receptor(PXR)-MDR1/P-gp dual luciferase reporter gene assay.In addition,Western Blot was used to detect the protein expression level of P-gp and the nuclear factor-κB(NF-κB)pathway related proteins.Results The relative expression levels of P-gp mRNA in experimental-M,-H groups and control group were 52.24±5.19,23.00±3.52 and 100.00±9.00;the relative expression levels of P-gp protein were 86.37±9.96,74.85±15.92 and 100.00±12.91;the relative activities P-gp luciferase were 230.19±41.32,203.10±52.84 and 279.67±19.20;the relative expression levels of p65(RelA/p65)in nucleus were 132.36±23.93,145.96±25.15 and 100.00±10.88;the relative expression levels of phosphorylation NF-κB inhibits protein kinase α/β(p-IKKα/β)in cytoplasm were 184.00±54.82,290.10±49.59 and 100.00±15.34;the relative expression levels of phosphorylated NF-κB inhibitory protein α(p-IκBα)in cytoplasm were 125.73±18.77,133.69±20.25 and 100.00±8.12;the relative expression levels of IκBα in cytoplasm were 78.36±14.83,70.44±14.57 and 100.00±22.82,respectively.The above indexes of experimental-M and experimental-H groups were compared with control group,and the differences were statistically significant(P＜0.05,P＜0.01,P＜0.001).Conclusion Epi can down-regulate the gene expression of P-gp in human colorectal adenocarcinoma cell line LS174T,and the mechanism may be related to activation of NF-κB and suppression of PXR.

Objective:To summarize the clinical characteristics of elderly patients with stage Ⅰ diffuse large B-cell lymphoma (DLBCL) and analyze the factors associated with prognosis.Methods:A case series study was conducted by retrospectively collecting clinical data from patients aged over 60 years with newly diagnosed stage Ⅰ DLBCL across 20 medical centers in Jiangsu Province, China, between June 2010 and April 2023. The involved site, classification and treatment plan were summarized. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan-Meier method, and Cox regression model.Results:The study included 255 patients with a median age of 69 years, of whom 130 (51.0%) were male, 66 (25.9%) were aged ≥75 years and 26 (10.1%) had a high Charlson Comorbidity Index (CCI) score of ≥2. Extranodal involvement was observed in 163 (63.9%) patients, with the stomach (37.4%, 61/163), intestine (19.0%, 31/163), testes (11.0%, 18/163), and breast (7.4%, 12/163) being the most frequently affected sites. The non-germinal center B-cell (non-GCB) subtype was prevalent in 63.7% of patients (142/223), with no significant difference between the nodal and extranodal groups ( P=0.681). Furthermore, 73.9% (184/249) and 11.7% (29/249) of patients received the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) and R-miniCHOP regimen, respectively. The overall 3-year PFS rate was 81.5%, and the 3-year OS rate was 85.6%. Patients aged ≥75 years ( HR=2.910, 95% CI 1.565-5.408, P=0.001) and/or with a CCI score ≥2 ( HR=2.324, 95% CI 1.141-4.732, P=0.020) had a significantly poorer PFS. Incorporating age ≥75 years and CCI score ≥2 into the stage-modified international prognostic index (sm-IPI) can better stratify the prognosis of elderly patients with stage Ⅰ DLBCL. The 3-year PFS rate was 48.7% in the high-risk group versus 85.7% in the low-risk group ( P<0.001). Conclusions:Our findings show that the elderly patients with stage Ⅰ DLBCL were predominantly characterized by extranodal involvement (particularly in the stomach and intestinal tract) and non-GCB subtype. Age ≥75 years and CCI ≥2 were identified as independent prognostic factors. The newly established sm-IPI-75-CCI incorporating these factors demonstrated superior prognostic discrimination compared to conventional risk assessment systems.

Objective To investigate the cognition of both doctors and patients in the neurology department of a tertiary hospital in Linyi City regarding graded diagnosis and treatment,and to explore its influencing factors.Methods Convenient sampling was used to select 456 survey subjects from 20 medical institutions within the medical consortium of a tertiary hospital in Linyi City,including 191 medical staffs and 265 patients;A total of 35 medical staffs and patients were interviewed.Results The overall awareness of graded diagnosis and treatment among medical staff and patients was(3.731±0.563)points and(3.136±0.367)points,respectively.The influencing factors of medical staff on grading diagnosis and treatment are professional title,years of work experience,and hospital grade.The influencing factors of patient on grading diagnosis and treatment are age,type of medical insurance,average monthly income,and whether they had a transfer experience.Conclusion While strengthe-ning information technology construction,government supervision and investment,and enhancing the capacity building of grassro-ots medical institutions,it is also necessary to actively promote the construction of close medical consortia to accelerate the forma-tion of a standardized and orderly new system for medical treatment and diagnosis.

BackgroundThe functional changes of the default mode network （DMN） are closely related to the onset of major depressive disorders. However， the relationship between the DMN subsystem （core subsystem， dorsomedial prefrontal cortex subsystem， medial temporal lobe subsystem） and symptoms of first-episode major depressive disorder remains unclear. ObjectiveTo investigate abnormal functional connectivity between DMN subsystems and the whole brain in first-episode major depressive disorder patients during the resting-state， and to analyse the correlations between these functional connectivity patterns and clinical symptoms， so as to reveal the potential neural mechanisms from the perspective of DMN subsystem. MethodsFrom September 2020 to September 2023， a total of 64 first-episode outpatients and inpatients meeting the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders， fourth edition （DSM-IV） were enrolled at the Inner Mongolia Autonomous Region Mental Health Center as the study group. During the same period， 54 healthy volunteers matched for age， gender， and years of education were recruited from the community as the control group. Both groups were assessed using the Hamilton Depression Scale-24 item （HAMD-24）. Resting-state functional magnetic resonance images （rs-fMRI） of the two groups were acquired using a Siemens 3.0 T scanner， and differences in functional connectivity between DMN subsystems （core subsystem， dorsomedial prefrontal cortex subsystem， medial temporal lobe subsystem） and the whole brain were compared. The functional connectivity values of brain regions with statistically significant differences between the two groups were extracted. Spearman's rank correlation coefficient analysis was used to investigate the correlation between these functional connectivity values and HAMD-24 scores of the study group. ResultsUltimately， 46 patients and 43 controls completed the study. Compared with the control group， the study group exhibited significantly stronger functional connectivity in the following pathways： between the right superior parietal lobule （core subsystem） and right cerebellar lobule VIII （t=3.954， P<0.05， GRF-corrected）， between the right lateral temporal cortex （dorsomedial prefrontal cortex subsystem） and right cerebellar lobule VIII， right and left hippocampi， right medial， and paracingulate gyrus （t=4.595， 4.208， 5.200， 4.038， P<0.05， GRF-corrected）， and between the temporoparietal junction （dorsomedial prefrontal cortex subsystem） and left lingual gyrus and right cerebellar lobule VIII （t=3.557， 4.274， P<0.05， GRF-corrected）. Conversely， weaker functional connectivity was observed between the right inferior frontal gyrus and left gyrus rectus （t=-3.824， P<0.05， GRF-corrected）. Furthermore， within the study group， the functional connectivity values between the right lateral temporal cortex and right hippocampus， as well as between the temporoparietal junction and right cerebellar lobule VIII， were both negatively correlated with the HAMD-24 cognitive impairment factor score （r=-0.306， -0.318， P<0.05）. ConclusionIncreased functional connectivity between the DMN （specifically its core and dorsomedial prefrontal cortex subsystems） and cerebellum， partial limbic system， and lingual gyrus may be associated with the neuropathology of first-episode major depressive disorder. Furthermore， alterations in functional connectivity between the dorsomedial prefrontal cortex subsystem and both the cerebellum and hippocampus in these patients may be related to cognitive function. ［Funded by 2019 Annual Inner Mongolia Autonomous Region Natural Science Foundation Project （number， 2019MS03038）； 2023 Annual Inner Mongolia Autonomous Region Natural Science Foundation Project （number， 2023MS08028）］