Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

Background: Visceral white adipose tissue (vWAT) inflammation is a critical pathology of obesity-caused heart damage and is closely associated with adipocyte endoplasmic reticulum (ER) dysfunction. Serine (or cysteine) peptidase inhibitor, clade A, member 3C (Serpina3c) has been identified as an adipokine with anti-vWAT inflammatory effects. However, it remains unclear whether Serpina3c deficiency promotion of vWAT inflammation involves adipocyte ER dysfunction and whether it further contributes to heart damage in obesity. Methods: Wild type and Serpina3c knockout (Serpina3c–/–) mice were fed a high-fat diet (HFD) for 12 weeks. An adeno-associated virus (AAV) was injected locally into epididymal white adipose tissue (eWAT) of Serpina3c–/– mice to induce eWAT-adipocyte- specific overexpression of Serpina3c (AAV-Serpina3c) or knockdown of hypoxia-inducible factor 1α (AAV-shHIF1α). In vitro experiments were performed in 3T3-L1 adipocytes. Results: Serpina3c–/– mice exhibited more severe eWAT, serum and heart inflammation after HFD feeding. Consistently, these adverse phenotypes were mitigated in AAV-Serpina3c and AAV-shHIF1α mice. Mechanistically, ER oxidoreductase 1α (Ero1α) and protein disulfide isomerase (PDI) family members PDIA3 and PDIA4 were found to be target genes of HIF1α. In the obese mice, Serpina3c deficiency caused adipocyte more hypertrophy, and activated HIF1α-Ero1α/PDI mediated ER overoxidation and ER stress in eWAT. Subsequently, this led to increased adipocyte apoptosis and chemokine production and decreased adiponectin expression, which promoted macrophage infiltration and M1 polarization in eWAT, thus exacerbating eWAT inflammation and ultimately facilitating serum and distal heart inflammation. Conclusion These findings indicate that Serpina3c is a significant regulator of adipocyte ER redox homeostasis, thus highlighting Serpina3c as a potential therapeutic target for obesity-related eWAT inflammation and heart damage.

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.

Since 1992， China has adopted a comprehensive strategy centered on universal infant hepatitis B vaccination. This approach has led to a significant reduction in the prevalence of hepatitis B surface antigen， particularly among younger age groups. Antiviral therapy not only improves the liver histology but also reduces the incidences of complications of cirrhosis and portal hypertension， as well as the risk of hepatocellular carcinoma. Clinical guidelines for the prevention and treatment of chronic hepatitis B have been periodically updated， and the prices of antiviral drugs have been substantially lowered， enhancing treatment accessibility and affordability. However， the HBV-related disease burden remains high in China due to its large population， the considerable number of individuals already chronically infected with HBV， and the low rates of diagnosis and treatment. To meet the global goal of eliminating viral hepatitis as a public health threat by 2030， large-scale testing and treatment of those already infected with HBV are critical.

AIM: To observe the changes of retinal nerve fiber layer(RNFL)thickness and radial peripheral capillary(RPC)density in patients with unilateral branch retinal vein occlusion(BRVO), and further analyze the correlation between RPC density and RNFL thickness.METHODS: Observational study. Totally 37 patients with unilateral BRVO diagnosed at the ophthalmology department of First Hospital of Qinhuangdao from October 2020 to January 2022 were selected, the 37 affected eyes were the unilateral BRVO group, and 37 fellow healthy eyes were the contralateral unaffected group, and 35 healthy individuals(35 right eyes were selected)without ocular diseases during the same period were selected as the normal control group. The best corrected visual acuity, intraocular pressure, anterior segment, fundus and optical coherence tomography angiography(OCTA)were examined in both eyes of all BRVO patients and healthy individuals. The central macular thickness(CMT), the RNFL thickness, and the optic disc-AV crossing distance(DAVD)were measured by built-in software of the OCTA equipment. The optimized U-net algorithm was used to eliminate the large blood vessels, and then the RPC density was calculated. The CMT, RNFL thickness and RPC density were compared among the three groups. And the correlations of the RPC density with the CMT, RNFL thickness, and the DAVD were investigated.RESULTS: Compared with the contralateral unaffected group and the normal control group, the CMT and the RNFL thickness were significantly thickened in the unilateral BRVO group(all P<0.05); there were no statistical differences in the CMT and the RNFL thickness between the contralateral unaffected group and the normal control group(all P>0.05). The RPC density in the unilateral BRVO group increased compared with the contralateral unaffected group and decreased compared with the normal control group, but there was no statistically difference(all P>0.05). However, the RPC density in the contralateral unaffected group decreased compared with the normal control group(P<0.05). The RPC density in the unilateral BRVO group was not correlated with the CMT(P=0.960), but positively correlated with the RNFL thickness(r=0.401, P=0.014)and negatively correlated with the DAVD(r=-0.339, P=0.040).CONCLUSION: The RNFL thickened significantly and the RPC density did not change significantly in the optic disc area of BRVO patients. The RPC density is positively correlated with the RNFL thickness, indicating that the RNFL thickness can be used as a monitoring indicator to analyze and study the damage degree of the RPC density.

BACKGROUND:At present,the incidence of scoliosis is increasing year by year,especially in adolescent idiopathic scoliosis.Therefore,it is more and more important to strengthen the research on the treatment of adolescent scoliosis. OBJECTIVE:To summarize the current status,hotspots,emerging trends,and frontiers of global research on the treatment of adolescent idiopathic scoliosis to provide reference and guidance for future related research. METHODS:The literature related to the treatment of adolescent idiopathic scoliosis was retrieved on the Web of Science Core Collection(WOSCC)database from 2013 to 2023.CiteSpace 6.2.R1 software was used for visual analysis of countries,institutions,authors,and keywords. RESULTS AND CONCLUSION:(1)A total of 561 English articles were included in this study.Among countries,institutions,and authors,the United States has contributed the most.Nanjing University and Qiu,Yong(Affiliated Drum Tower Hospital,Nanjing University School of Medicine)are the most published institution and author.The academic journal with the largest number of articles is the European Spine Journal.(2)In the analysis of cited literature,the top 10 most cited articles mainly describe the effects of surgical treatment and conservative treatment on improving adolescent idiopathic scoliosis,especially improving the curvature of patients.(3)Through the summary of highly cited articles and the keyword clustering,keyword prominence in-depth mining,the research hotspots are currently the relationship between Cobb angle and treatment choice,the therapeutic effect of exercise therapy and the therapeutic effect of posterior vertebral fusion.(4)The prognosis of patients with different curvatures has not been studied in depth,and the etiology of adolescent idiopathic scoliosis has not been clarified,so the relationship between curvature and prognosis and the etiology of adolescent idiopathic scoliosis may be a new research trend in the future.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective:To investigate the miRNA-mRNA regulatory network in a rat model of Tourette syndrome(TS)using transcriptomic technology and to screen key signaling pathways and potential traditional Chinese medicine(TCM)candidates for intervention.Methods:A TS rat model was established using iminodipropionitrile(IDPN).RNA sequencing was performed to identify differentially expressed miRNAs and mRNAs in the brain tissues of TS rats.Bioinformatics analysis was applied to construct interaction networks,and network pharmacology was further employed to screen potential TCM compounds.Results:After 7 days of IDPN modeling,the model group exhibited motor and stereotypical behavioral changes,with behavioral scores greater than 3 points.Hema toxylin-eosin(HE)staining revealed irregular neuronal nuclear morphology,uneven chromatin distribution,nuclear pyknosis,and increased glial cell density.KEGG enrichment analysis identified key pathways:calcium signaling pathway,neuroactive ligand-receptor interaction,p53 signaling pathway,ECM-receptor interaction,and TGF-β signaling pathway.miR-125a-3p,miR-106-3p,and miR-760-3p were identified as pivotal miRNAs.Potential TCM candidates included Ajuga decumbens,Acanthopanax bark,Codonopsis pilosula,Stephania japonica,Os Draconis,Notopterygium root,Siraitia grosvenorii,Zanthoxylum nitidum root,Morinda officinalis,and Corydalis yanhusuo.Conclusion:The miRNAs miR-106-3p,miR-125a-3p,and miR-760-3p may mediate TS pathogenesis by altering critical signaling networks,including the calcium signaling pathway,neuroactive ligand-receptor interaction,and ECM-receptor interaction pathways,leading to neuroimmune inflammation and dopaminergic system dysregulation.TCM compounds such as Corydalis yanhusuo and Ajuga decumbens may exert therapeutic effects through multi-component synergistic regulation of these miRNAs and downstream pathways.