Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

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.

Inflammatory bowel disease (IBD) is a chronic disorder characterized by recurrent mucosal inflammation. Although biologics and small-molecule therapies can effectively suppress inflammation, only 13%-34% of patients achieve deep histological remission, and most experience relapse within 2-3 years. Existing biomarkers, including C-reactive protein (CRP), fecal calprotectin, and endoscopic measures, have limited ability to detect subclinical molecular abnormalities, suggesting that disease-driving factors may persist even during clinical remission. Recently, Pang et al. reported a study in Science that investigated the role of epithelial cell-death signaling using a prospective adult IBD cohort comprising 80 participants and more than 900 biopsy specimens. By integrating bulk and spatial transcriptomics, patient-derived organoids, and 24-36 months of longitudinal follow-up, the authors characterized epithelial cell-death programs associated with IBD persistence. They found that the necroptosis-associated marker pRIPK3 could be detected in the absence of overt histological inflammation and persisted during advanced therapy. IFN‑γ and TNF reshaped the transcriptional landscape of specific colonic epithelial cells, inducing an inflammatory macrophage-like program termed cluster B and triggering RIPK1-independent RIPK3 signaling. This was followed by NOS2/iNOS-mediated mitochondrial apoptosis driving colonocyte death, whereas PUMA-dependent apoptosis contributed to intestinal stem-cell loss. Higher baseline pRIPK3 levels were associated with subsequent disease activity, although their independent predictive value remains to be established. These findings suggest that persistent IBD may reflect not only residual immune inflammation but also inflammation-induced pathological epithelial states. Future therapeutic strategies may therefore need to extend beyond inflammation control toward “molecular repair”, including targeting noncanonical RIPK3 signaling, modulating iNOS/PUMA-dependent mitochondrial apoptosis, and developing relapse-risk stratification strategies based on tissue biomarkers such as pRIPK3. Such approaches may help shift the therapeutic goal from “clinical remission” toward “molecular healing”.

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.

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.

Aim To explore the mechanism of Jiawei Shaofu Zhuyu decoction in antagonizing endometriosis fibrosis by regulating mitophagy.Methods After the animal model was constructed,the syndrome was evalu-ated by general condition,organ water content and ther-mal imaging.The curative effect was evaluated by the weight of ectopic focus and the degree of adhesion.The pathological changes were compared using HE stai-ning,transmission electron microscopy,Masson and Sir-ius red staining.The expression of PINK1 and Parkin was detected by immunohistochemistry.The expression of mRNA and protein was determined by qPCR and Western blot,and the level of serum ROS was detected by ELISA.Results The autonomic activity of model mice was weakened,the water content of organs rose,and the temperature of limbs and lower abdomen was reduced by thermal imaging.HE staining showed obvi-ous hyperplasia of ectopic epithelium and glands.Transmission electron microscopy showed mitochondrial and endoplasmic reticulum structure damage,and nor-mal autophagy structure disappeared.Masson and Siri-us red staining showed increased collagen deposition;immunohistochemistry showed decreased expression of PINK1 and Parkin in ectopic foci.qPCR and Western blot showed that the expression of PINK1,Parkin,Bec-lin1,LC3 mRNA and protein in ectopic foci of model mice decreased,the expression of p62 mRNA and pro-tein increased,and serum ROS increased.The syn-drome performance of model mice was improved after the intervention of Jiawei Shaofu Zhuyu decoction;the inflammatory infiltration of ectopic foci was relieved,the morphology of mitochondria and endoplasmic retic-ulum was restored,and normal autophagy structure ap-peared.The degree of collagen deposition and fibrosis was reduced;the mRNA and protein expression of PINK1,Parkin,Beclin1 and LC3 increased.The ex-pression of p62 mRNA and protein decreased,and the level of ROS decreased.Conclusions Jiawei Shaofu Zhuyu decoction can improve the fibrosis of ectopic le-sions in mice with endometriosis of cold-dampness sta-sis syndrome,which may be related to the regulation of mitophagy.

Objective To investigate the risk factors and clinical significance of layered plaques that were detected by optical coherence tomography(OCT)in patients with angiographically intermediate coronary lesions,and relationship with prognosis.Methods This was a signal-center retrospective study focusing on patients whom underwent coronary angiography and OCT.The layered plaque group and non-layered plaque group were divided according to the presence or absence of stratified plaque.Clinical data,laboratory indicators,angiography,and OCT results were collected and compared between the two groups.Using logistic regression to analyze the relationship between stratified plaques and clinical features;Cox regression analysis was used to investigate the influencing factors of cardiovascular adverse events in patients with critical coronary artery disease.Results A total of 172 patients were enrolled,including 96 patients in non-layered plaque group and 76 patients in layered plaque group.Male(OR 2.415,95％CI 1.162-5.020,P=0.018),diabetes(OR 2.505,95％CI 1.137-5.525,P=0.023)and history of hyperlipidemia(OR 3.590,95％CI 1.478-6.333,P=0.003)were independent risk factors for stratified plaque.In OCT analysis,the proportion of thin-cap fibroatheroma(TCFA)plaque,macrophage infiltration,microvascularization,thrombosis,plaque rupture,and intimal dissection,as well as lipid plaque length,lipid plaque arc,and lipid plaque index were higher in the layered plaque group.After adjusting for other risk factors,macrophage infiltration is independently associated with stratified plaques(OR 2.106,95％CI 1.019-4.353,P=0.044).Kaplan-Meier survival analysis showed that the target lesion revascularization rate in the layered plaque group was higher than that in the non-layered plaque group(Log-rank P=0.030).Cox regression analysis shows that it has both stratified plaque and thin fibrous membrane plaque characteristics was an independent predictor of cardiovascular adverse events(HR 5.165,95％CI 1.696-15.727,P=0.004).Conclusions In patients with angiographically intermediate coronary lesions,OCT detection of stratified lesions is often accompanied by other unstable plaque features,indicating an increased risk of adverse cardiovascular events.Simultaneously possessing features of stratified plaques and TCFA is an independent predictor of adverse cardiovascular events in patients with critical coronary artery disease.

The exoskeleton robot for lower limb medical rehabilitation in foreign countries and China was introduced in terms of the research status,structure and working principle,and analysis was carried out over its key technologies.It's pointed out the exoskeleton robot for lower limb medical rehabilitation would be enhanced in energy endurance,safety and comfort,individualized and intelligent control,modularity and lightweight design.[Chinese Medical Equipment Journal,2025,46(1):88-100]

Objective:To investigate the relationship between the severity of pharyngolaryngeal sensory impairment and swallowing biomechanics as well as the risk of penetration-aspiration in patients with dysphagia following infratentorial stroke.Methods:This retrospective cross-sectional study enrolled 51 patients with dysphagia following infratentorial stroke hospitalized in the Department of Rehabilitation Medicine of The Third Affiliated Hospital of Sun Yat-sen University between January 2022 and December 2023. Participants were categorized into three groups: normal sensation group [15 males, 2 females; age range 29-76 (56.0±13.3)years], diminished sensation group[16 males, 3 females; age range 38-80(62.0±11.8)years], and absent sensation group [14 males, 1 female; age range 44-75 (60.0±9.7)years]. All patients underwent laryngoscopy and videofluoroscopic swallowing study, which included pharyngolaryngeal sensory testing and Penetration-Aspiration Scale assessment. Swallowing temporal parameters were quantitatively analyzed. Group comparisons for different variable types were conducted using the Chi-square test, one-way ANOVA, and the Kruskal-Wallis test. The correlation between sensory groups and Penetration-Aspiration Scale scores was assessed using Spearman′s correlation analysis. Logistic regression was employed to analyze the impact of pharyngolaryngeal sensory function on penetration-aspiration events.Results:Among the 51 patients, 33.33% (17/51) had normal pharyngolaryngeal sensation, while, 66.67% (34/51) exhibited sensory impairment. The normal sensation group exhibited a significantly longer laryngeal vestibule closure (LVC) time [792 (643, 1 205) ms] compared to the diminished [528 (380, 776) ms] and absent sensation groups [380 (322, 404) ms] ( H=6.502, P=0.039). Additionally, the upper esophageal sphincter opening time was longer in the normal sensation group than in the absent sensation group [528 (371, 710) ms vs 182 (0, 710) ms, H=6.003, P=0.049]. Correlation analysis indicated a significant negative correlation between the severity of sensory impairment and Penetration-Aspiration Scale scores ( r=-0.366, P=0.008). Logistic regression analysis demonstrated that greater sensory impairment was an independent risk factor for penetration-aspiration ( OR=9.29, 95%CI=1.57-54.77, P=0.014). Conclusion:Pharyngolaryngeal sensory deficits are common after infratentorial stroke dysphagia and are significantly associated with impaired swallowing biomechanics and increased aspiration risk. The severity of sensory deficit is a key determinant of penetration-aspiration risk, highlighting its value in risk stratification and therapeutic decision-making for dysphagia.

AIM:The study aims to investigate the effects of Huayu-Qutan formula(HYQT)-medicated serum on oleic acid-induced lipid damage and endoplasmic reticulum stress(ERS)in HepG2 cells,and to explore the mecha-nism underlying the prevention and treatment of lipid metabolism disorders by HYQT.METHODS:The HepG2 cells were treated with various concentrations of oleic acid,and CCK8 assay,oil red O staining and ELISA were used to identify the optimal treatment concentration and time of oleic acid and HYQT-medicated serum.Moreover,the cells were divided into control,model,thapsigargin(Tg),and Tg+HYQT groups.Cellular lipid deposition was measured using oil red O staining,while triglyceride(TG)and free fatty acid(FFA)levels were assessed by ELISA.Transmission electron micros-copy was used to examine endoplasmic reticulum structures,and RT-qPCR and Wes fully automated protein quantification analysis system were used to measure the mRNA and protein expression of glucose-regulated protein 78(GRP78),sterol regulatory element binding protein 1c(SREBP1c),acetyl coenzyme A carboxylase(ACC1),fatty acid synthase(FAS)and stearoyl coenzyme A desaturase 1(SCD1)in different groups.RESULTS:Significant lipid deposition was induced in HepG2 cells after treatment with 1 000 μmol/L oleic acid,while treatment with serum containing 10%HYQT for 48 h was found to be optimal.Compared with control group,the cells in model group showed significant deposition of oil red O-stained lipid droplets in the cytoplasm,associated with endoplasmic reticulum expansion,ERS,and nuclear condensa-tion.The TG and FFA levels,and the mRNA and protein expression levels of GRP78,SREBP1c,ACC1,FAS and SCD1 were increased significantly(P<0.01).Compared with model group,the cells treated with HYQT-mediated serum showed marked decrease in the number of lipid droplets in the cytoplasm,with restored endoplasmic reticulum morphology.The TG and FFA levels were significantly reduced(P<0.01),and the mRNA and protein levels of factors related to ERS and de novo fatty acid synthesis were markedly decreased(P<0.01).Treatment with Tg,an ERS agonist,led to greater accu-mulation of cytoplasmic lipid droplets and increased endoplasmic reticulum expansion.Marked variations in the morpholo-gy and size of the endoplasmic reticulum were observed,with fusion and clustering of vacuoles.The TG and FFA levels,and the expression of ERS-and fatty acid synthesis-related factors were increased(P<0.01).Compared with Tg group,the cells treated with Tg+HYQT showed reduced number of cytoplasmic lipid droplets,attenuated endoplasmic reticulum dilation,and decreased number and volume of vacuoles,while the TG and FFA levels and the expression of ERS-and fatty acid synthesis-associated factors were significantly decreased(P<0.01).CONCLUSION:Serum containing HYQT alle-viates oleic acid-induced lipid damage in HepG2 cells by inhibiting ERS-induced de novo synthesis of fatty acids.