ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

ObjectiveTo investigate the independent risk factors for decompensation in patients with liver cirrhosis， to construct a nomogram-based risk assessment model， and to assess its risk assessment performance and clinical value by comparing it with Model for End-Stage Liver Disease （MELD）， MELD combined with serum sodium concentration （MELD-Na）， and MELD 3.0 scoring system. MethodsA retrospective analysis was performed for 514 patients with liver cirrhosis who attended The First Hospital of Shanxi Medical University from January 2020 to May 2025， and related data were collected， including demographic data and laboratory markers. According to the presence or absence of decompensation， the patients were divided into compensation group with 275 patients and decompensation group with 239 patients. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator regression analysis was used for screening of variables， and the multivariate logistic regression analysis was used to identify independent risk factors for decompensated liver cirrhosis and construct a nomogram model. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis were used to assess the discriminatory ability， calibration， and net clinical benefit of the model. ResultsThe multivariate logistic regression analysis showed that low hemoglobin （odds ratio ［OR］=0.984， 95% confidence interval ［CI］： 0.969　—　0.999， P<0.05）， low lymphocytes （OR=0.564， 95%CI： 0.383　—　0.830， P<0.05）， high international normalized ratio （OR=3.131， 95%CI： 1.242　—　7.891， P<0.05）， and low serum sodium （OR=0.922， 95%CI： 0.872　—　0.975， P<0.05） were independent risk factors for decompensation events in patients with liver cirrhosis. Construct the prediction model equation： Logit（P）=intercept value－0.016×hemoglobin－0.573×lymphocytes+1.141×INR－0.081×serum sodium. The nomogram constructed based on these factors had a good discriminatory ability， with an area under the ROC curve （AUC） of 0.787， a specificity of 77.8%， and a sensitivity of 67.4%， and it had significantly better predictive performance than MELD score （AUC=0.718）， MELD-Na score （AUC=0.719）， and MELD 3.0 score （AUC=0.725）. The calibration curve showed good consistency between the probability of risk assessed by the model and the observed probability. The decision curve analysis showed that compared with the MELD-based scores， this model provided higher net clinical benefit across a wide range of risk thresholds. ConclusionThis study successfully constructed and validated a nomogram risk assessment model incorporating hemoglobin， international normalized ratio， lymphocytes， and serum sodium. This model has good clinical practicability and can help to achieve early identification of high-risk patients with decompensated liver cirrhosis and optimize intervention strategies in clinical practice.

[This corrects the article DOI: 10.1016/j.apsb.2018.08.009.].

Objective To explore the value of deep learning(DL)model based on grayscale ultrasound for predicting asymptomatic advanced chronic liver disease(cACLD).Methods Totally 258 patients with asymptomatic compensatory chronic liver diseases were retrospectively included,among them 117 with F3 or F4 stage liver fibrosis were classified into cACLD group,while 141 with F1 or F2 stage liver fibrosis were taken as non-cACLD group.The patients were divided into training set(n=180,including 82 cases of cACLD and 98 cases of non-cACLD)and validation set(n=78,including 35 cases of cACLD and 43 cases of non-cACLD)at the ratio of 7∶3.Univariate and multivariate logistic regression were used to screen independent clinical predictors of cACLD and construct a clinical model.Based on liver grayscale ultrasound,optimal DL features were extracted and screened,and Resnet50 network was adopted as framework,na?ve Bayes classifier was used to construct DL model,and a combined model was constructed based on clinical model and DL model.The efficacy and clinical value of each model for predicting asymptomatic cACLD were evaluated.Results Age,gamma-glutamyl transferase and platelet count were all independent clinical predictors of cACLD,and a clinical model was constructed.Totally 38 optimal DL features were screened to build a DL model.The AUC of combined model in training set and validation set was 0.950 and 0.740,of DL model was 0.944 and 0.737,respectively,being not significantly different(both P＞0.05)but all higher than that of clinical model(0.667 and 0.573,all P＜0.05).Taken 0.59-0.90 as the threshold,the net benefits of combined model in both training and validation sets were higher than that of other models.Conclusion DL model based on grayscale ultrasound could be used to effectively predict asymptomatic cACLD.Combining with clinical characteristics might improve clinical net benefit of this model.

Objective To explore the constituent elements of patients'sense of gain experience and provide theoretical insights for improving patient satisfaction with healthcare services.Methods Purposive sampling and convenience sampling were employed to select 46 experts in health management from universities,hospitals,government agencies,and academic societies across China for semi-structured interviews.Qualitative analysis of the interview data was conducted using grounded theory.Results Through three-level coding,159 initial concepts,19 categories,7 main categories,and 2 core categories were identified.A conceptual framework for the constituent elements of patients'sense of gain experience was constructed,centered on two core dimensions:micro-level experience and macro-level acquisition.Conclusion The concept of"patients'sense of gain experience"enriches the theoretical framework of"sense of gain"and breaks through the unidimensional approach to evaluating medical service quality.Its enhancement requires collaborative efforts from multiple stakeholders,including medical institutions,governments,policymakers,and other actors.

ObjectiveTo investigate the awareness and clinical practice of guidelines for the prevention and treatment of chronic hepatitis B （2022 edition） among clinicians. MethodsFrom July 19 to December 31， 2024， a self-designed electronic questionnaire was distributed via the WeChat mini program to collect related data from 1 588 clinicians nationwide， including their awareness and practice based on 18 questions regarding testing and referral， diagnosis and treatment， and follow-up. ResultsAmong all respondents， only 350 clinicians correctly understood all the updated key points of antiviral indications and treatment for special populations in the 2022 edition of guidelines for the prevention and treatment of chronic hepatitis B， with an overall awareness rate of 22.0%. Only 20% ‍—‍ ‍40% of the patients with positive HBV DNA and an age of >30 years receive antiviral therapy， while 80% ‍—‍ ‍100% of the patients with positive HBV DNA and a family history of hepatitis B cirrhosis or hepatocellular carcinoma receive antiviral therapy. The median follow-up rates at 1 year， 3 years， and 5 years were 67.5% 57.5% and 47.5%，respectively， showing a trend of gradual reduction， which might be associated with the influencing factors such as insufficient time for follow-up management by clinicians， insufficient awareness of the disease among patients， and poor adherence to follow-up. ConclusionThere is a gap between the awareness and practice of guidelines for the prevention and treatment of chronic hepatitis B （2022 edition） among clinicians. It is recommended to further strengthen training and focus on the whole process of “detection， diagnosis， treatment， and management” for patients with chronic hepatitis B in healthcare institutions， in order to promote the implementation of the guidelines.

Natural antimicrobial peptides (AMPs) are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens. They have found extensive applications in the fields of medicine, food, and agriculture. However, efficiently screening AMPs from natural sources poses several challenges, including low efficiency and high antibiotic resistance. This review focuses on the action mechanisms of AMPs, both through membrane and non-membrane routes. We thoroughly examine various highly efficient AMP screening methods, including whole-bacterial adsorption binding, cell membrane chromatography (CMC), phospholipid membrane chromatography binding, membrane-mediated capillary electrophoresis (CE), colorimetric assays, thin layer chromatography (TLC), fluorescence-based screening, genetic sequencing-based analysis, computational mining of AMP databases, and virtual screening methods. Additionally, we discuss potential developmental applications for enhancing the efficiency of AMP discovery. This review provides a comprehensive framework for identifying AMPs within complex natural product systems.

Hepatitis B virus (HBV) infection represents a significant global health challenge. Current therapeutic options, such as interferon and nucleoside analogues (NAs), are limited by issues including long-term medication toxicity, the virus's propensity to develop drug resistance, and the inability to eradicate covalently closed circular DNA (cccDNA). Core protein allosteric modulators (CpAMs), as an emerging class of anti-HBV drugs, target HBcAg to interfere with capsid assembly. This not only blocks viral nucleocapsid formation and inhibits viral replication but also indirectly destabilizes the cccDNA pool, while exhibiting a relatively high genetic barrier to resistance. This article systematically evaluates HBV drug resistance to CpAMs and proposes anti-resistance strategies, including the combination of nucleoside analogues with CpAMs, enhancing protein-drug interactions, targeting HBcAg degradation, designing multi-target inhibitors, and employing combination therapy. Looking ahead, the integration of structural biology, computational chemistry, and immunotherapy will offer innovative approaches for developing novel, highly effective, and low-resistance inhibitors, thereby advancing the functional cure of hepatitis B.

Natural antimicrobial peptides(AMPs)are promising candidates for the development of a new gener-ation of antimicrobials to combat antibiotic-resistant pathogens.They have found extensive applications in the fields of medicine,food,and agriculture.However,efficiently screening AMPs from natural sources poses several challenges,including low efficiency and high antibiotic resistance.This review focuses on the action mechanisms of AMPs,both through membrane and non-membrane routes.We thoroughly examine various highly efficient AMP screening methods,including whole-bacterial adsorption binding,cell membrane chromatography(CMC),phospholipid membrane chromatography binding,membrane-mediated capillary electrophoresis(CE),colorimetric assays,thin layer chromatography(TLC),fluorescence-based screening,genetic sequencing-based analysis,computational mining of AMP data-bases,and virtual screening methods.Additionally,we discuss potential developmental applications for enhancing the efficiency of AMP discovery.This review provides a comprehensive framework for identifying AMPs within complex natural product systems.

Objective Feasibility application of microelectrode recording(MER)during sub thalamic nucleus deep brain stimulation(STN-DBS)implantation under desflurane general anesthesia(GA)in patients with Par-kinson's disease(PD).Methods A prospective cohort of 20 PD patients undergoing STN-DBS under desflurane general anesthesia were enrolled.Intraoperative MER quality,pos-operative acute pain,cognitive function,anxi-ety/depression status,quality of life,and clinical efficacy of DBS were evaluated.Results Among the patients,14 were male with average PD duration of(8.1±3.6)years.Hoehn-Yahr staging averaged 2.8±0.5 in"on"state and 2.3±0.5 in"off"state.The mean DBS surgery duration was 87.4 minutes.Highly normalized root-mean-square(NRMS)signals were successfully recorded in all cases,with remedial measures applied in 4 pa-tients to achieve satisfactory MER signals.Post-operative Visual Analogue Scale(VAS)pain scores on days 1,2,and 3 were 3.7±2.2,2.8±1.6,and 1.8±2.0,respectively.Montreal Cognitive Assessment(MoCA)scores showed no statistical difference during hospitalization as compared to pre-operative values,but significantly de-creased at 6-month follow-up(24.3±4.1 vs.21.5±3.5,P＜0.05).All patients demonstrated significant reduc-tion in Hamilton Anxiety Scale(HAMA),Hamilton Depression Rating Scale(HAMD),and Parkinson's disease Questionnaire-39(PDQ-39)scores at 6-month follow-up.The unified Parkinson's disease rating scale(UPDRS-Ⅲ)improvement rates were 51.4%±39.2%(medication-on)and 61.6%±26.8%(medication-off)respectively with Levodopa Equivalent Daily Dose(LEDD)improvement rate of 48.6%±23.0%.Conclusions Desflurane general anesthesia is safe and feasible for electrods implantation in STN-DBS of PD patients,without interfering with intra-operative MER or postoperative outcomes.