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.

ObjectiveTo observe the clinical efficacy and safety of Tianmeng Lingjiao Formula（天礞羚角方， TLF） as an adjunctive therapy for paroxysmal atrial fibrillation （PAF） with phlegm-heat harassing the interior syndrome. MethodsNinety patients with PAF and phlegm-heat harassing the interior syndrome were enrolled and randomly assigned in a 2∶1 ratio to test group （60 cases） and control group （30 cases）. The control group received conventional western medicine， while the test group received TLF in addition to the conventional treatment， with one dose administered daily. The treatment course for both groups was 4 weeks. The efficacy of traditional Chinese medicine （TCM） syndrome and the rate of atrial fibrillation （AF） conversion were compared between groups. Assessments included AF burden， frequency and duration of AF episodes， TCM syndrome scores， and European Heart Rhythm Association （EHRA） AF symptom classification before and after treatment. Safety indicators such as blood routine， urine routine， stool routine， liver function， kidney function， and electrolytes were also monitored. Adverse events were recorded. ResultsA total of 82 patients completed the study （54 in the test group and 28 in the control group）. After treatment， AF burden， the frequency and duration of AF episodes， and TCM syndrome scores were significantly reduced in both groups， and the test group showed lower indicators than the control group （P＜0.01 or P＜0.05）. The AF conversion rate was 25.93% （14/54） in the test group， significantly higher than 3.57% （1/28） in the control group （P＜0.05）. The total effective rate for TCM syndrome efficacy was 83.33% （45/54） in the test group， which was significantly higher than 67.86% （19/28） in the control group （P＜0.05）. After treatment， the EHRA atrial fibrillation symptom severity in the test group showed significant improvement， and the improvement in atrial fibrillation symptoms was superior to that in the control group （P＜0.01or P＜0.05）. No significant abnormalities were observed in safety indicators， and no adverse events occurred during the trial. ConclusionTLF as an adjunctive therapy can reduce AF burden， enhance the conversion rate of AF， decrease the frequency and duration of episodes， and improve clinical symptoms in patients with paroxysmal atrial fibrillation presenting with phlegm-heat harassing the interior syndrome， with a favorable safety profile.

Objective To explore the effects of breast-conserving surgery (BCS), simple mastectomy (SM), and modified radical mastectomy (MRM) on early postoperative upper limb motor function, physical and psychological status in breast cancer patients. Methods A total of 212 female breast cancer patients undergoing surgery at the Department of Breast Surgery of Zhangzhou Affiliated Hospital of Fujian Medical University from October 2024 to December 2025 were enrolled. Patients were divided into the BCS group (n＝34), SM group (n＝79), and MRM group (n＝99). General demographic and clinical data were collected. At 3–7 days postoperatively, the Constant-Murley score (CMS), active range of motion (AROM) of the shoulder joint, and the Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH) score were used to assess upper limb function; the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), Functional Assessment of Cancer Therapy-Breast (FACT-B), and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate patients’ psychological status, quality of life, and sleep quality. Results The total CMS score, pain score, and range of motion score in the BCS group were higher than those in the SM and MRM groups (P＜0.05). The BCS group showed greater forward flexion range of motion than that in the SM and MRM groups (P＜0.05); and the SM group showed smaller internal rotation range of motion than that in the MRM and BCS groups (P＜0.05). The PHQ-9 depression score in the BCS group was significantly higher than that in the MRM group (P＜0.05). There were no statistically significant differences among the three groups in QuickDASH, GAD-7, FACT-B, or PSQI scores. Conclusions Early postoperative upper limb function and depression status varied among breast cancer patients undergoing different surgical procedures. Patients undergoing BCS had better preservation of upper limb function but higher depression scores.

Objective To analyze the characteristics and risk factors associated with postoperative deep vein thrombosis(DVT)of the lower extremities in patients undergoing surgery for lumbar degenerative diseases.Methods A retrospective analysis was conducted on clinical data from 298 patients who were hospitalized for lumbar degenerative diseases and underwent lumbar spine surgery treatment in the First Affiliated Hospital of Nanchang University from October 1,2022 to April 15,2023.Patients were divided into DVT group(n=71)and non-DVT group(n=227)according to whether DVT of the lower limbs occurred within 1 week postoperatively.The incidence and distribution characteristics of postoperative DVT were analyzed.Univariate and binary logistic regression analyses were performed to identify risk factors for DVT,and receiver operating characteristic(ROC)curves were used to determine cut-off values for relevant risk factors.Results A total of 298 patients were included,among whom 159 were males(53.4%)and 139 were females(46.6%),with an average age of(64.5±9.8)years.DVT occurred in 71 patients,and the incidence of lower extremity DVT was 23.8%.In the DVT group,there were 49 cases(69.0%)of intermuscular vein thrombosis,and 22 cases of other types of thrombosis(7 cases of peroneal vein thrombosis,4 cases of posterior tibial vein thrombosis,3 cases of common femoral vein thrombosis,1 case of anterior tibial vein thrombosis,and 7 cases of multiple thrombosis);58 cases(81.7%)had DVT in one lower extremity,and 13 cases(18.3%)had DVT in both lower extremities.Univariate analysis results showed that age,body mass index(BMI),length of hospital stay,history of hypertension,operative time,and intraoperative blood loss were associated with the occurrence of lower extremity DVT after surgery for lumbar degenerative diseases(P<0.05).Binary logistic regression analysis results indicated that older age(OR=1.079,P<0.01),higher BMI(OR=1.130,P=0.01),history of hypertension(OR=2.992,P<0.01),and larger intraoperative blood loss(OR=1.002,P=0.03)were independent risk factors for the occurrence of lower extremity DVT.ROC curve analysis demonstrated that patients with age>58.5 years,BMI>24.01 kg/m2,history of hypertension,and intraoperative blood loss>550 ml had a significantly increased risk of postoperative lower limb DVT.Conclusions The incidence of lower extremity DVT after surgery for lumbar degenerative disease is high,and intermuscular venous thrombosis is more common.Older age,higher BMI,history of hypertension,and larger intraoperative blood loss are independent risk factors for the occurrence of lower extremity DVT after surgery.

Objective To explore the status of nursing work engagement, nursing work environment and emotional intelligence and their relationship among nurses who work in hematopoietic stem cell transplantation (HSCT) units. Methods A total of 225 HSCT nurses were selected as study subjects by convenience sampling method. Utrecht Work Engagement Scale, Nursing Work Environment Scale and Emotional Intelligence Scale were used to assess the work engagement, work environment and emotional intelligence among these nurses. AMOS 23.0 software was used to construct the structural equation model. Results The median and 25th and 75th percentiles of the score of work engagement of the research subjects were 59.0 (54.0, 64.0) points. The average scores of the nursing work environment and emotional intelligence were (117.8±21.5) and (58.8±10.7) points, respectively. The score of work engagement was positively correlated with the scores of the nursing work environment and emotional intelligence (rank correlation coefficients were 0.550 and 0.431, respectively, both P<0.01). The total score of the nursing work environment was positively correlated with the total score of emotional intelligence (correlation coefficient was 0.271, P<0.01). The nursing work environment influenced the work engagement status of HSCT nurses through the mediating effect of emotional intelligence, with an indirect effect of 0.115 (95% confidence interval: 0.201-0.365), accounting for 20.4% of the total effect. Conclusion Emotional intelligence is a mediating variable between the nursing work environment and work engagement of HSCT nurses.

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.

Objective To investigate the status of management of iodinated contrast media(ICM)extravasation in Henan Province,as well as nurses' knowledge and influencing factors,in order to provide a basis for optimizing management strategies.Methods A self-designed questionnaire was applied,employing convenience sampling,to survey nursing administrators and nurses in the radiology departments of 55 tertiary hospitals across 16 regions of Henan Province,from December 2024 to January 2025.Multiple linear regression analysis was conducted to explore the factors influencing nurses' knowledge.Results A total of 55 nursing administrators and 64 nurses participated,with a valid questionnaire response rate of 100％.The survey results reveal that only 5.45％of radiology depart-ments utilized high-pressure central venous catheters,and 32.73％employed vascular visualization techniques.When setting the high-pressure injection speed for ICM,only 54.55％of radiology departments required an assessment of the type and model of intravenous access.Additionally,only 9.09％of radiology departments mandated an observa-tion for 2 to 4 hours following ICM extravasation.Furthermore,only 50.91％of radiology departments had estab-lished an information system for ICM use.The nurses' knowledge score regarding the prevention and management of ICM extravasation was(90.00±17.59),influenced by years of experience in radiology and professional titles(P＜0.05).Conclusion The prevention and management measures for ICM in radiology departments in Henan Province need further improvement.Nursing administrators should optimize management strategies,improve relevant training systems,and continuously enhance nurses' knowledge and practical abilities.

Objective To study the epidemiological characteristics of special pathogenic infection in Wenchang area following the super typhoon"Yagi"disaster,and provide basis for the diagnosis,treatment,prevention and control of infectious diseases in disaster-affected areas.Methods Clinical characteristics and pathogenic data of 7 groups of patients infected with 9 species of zoonotic pathogens and opportunistic pathogens were analyzed retrospectively.The 7 groups included:pre-typhoon landfall group(August 6 to September 5,2024),post-typhoon landfall group(September 6 to October 5,2024),and groups in the past years of the same period(A2023,B2022,C2021,D2020,E2019,September 6 to October 5 of each year in 2019-2023).Epidemiological characteristics of patients as well as distribution and resistance of pathogens were compared and analyzed.Results In post-typhoon landfall group,26 patients were infected.The overall infection rate of the post-typhoon landfall group was higher than all groups except B2022 group(all P＜0.05).The infected cases mainly distributed in coastal areas.The main route of infection was outside the hospital(88.5％).Male accounted for 80.8％,agricultural workers accounted for 53.9％,and 69.2％of the cases occurred within 10 days after the typhoon.The major infection sites were multiple site co-infection,bloodstream infection,and soft tissue infection.The main pathogens maintained high sensitivity to commonly used antimicrobial agents.The accuracy of clinical initial empirical antimicrobial use was relatively low(45.5％in post-typhoon landfall group vs 62.8％in the groups of the same period in the past).The clinical cure rate decreased to 76.9％,and mortality increased to 7.7％.The mortality of patients infected with Burkholderia pseudomallei and kidney infected with Leptospira were 25.0％and 50.0％,respectively.Conclusion After ty-phoon disaster,special pathogen infection significantly increases and the prognosis is poor.It is recommended to emphasize blood culture and molecular biology testing to facilitate early diagnosis and precise treatment,optimize prevention and control measures,and enhance the accuracy of clinical empirical medication.