ObjectiveA mouse model of vaccinia virus Tiantan strain (VTT)-induced encephalopathy was developed using AG6 mice. MethodsVTT was amplified by infecting Vero cells at a multiplicity of infection (MOI) of 0.01, followed by concentration and titration. After 72 h of incubation, virus-containing cells were collected and subjected to concentration. The concentrated viral suspension was serially diluted (10-fold dilutions) and added to 6-well plates containing confluent Vero cell monolayers for plaque assay. The number of plaques formed in each well was counted, and the virus titer was calculated based on the dilution factor. Fourteen 5-6-week-old AG6 mice (half male and half female, housed separately by sex) were randomly divided into a control group (n=3, PBS), a low-dose group (n=6, 1×10⁵ PFU), and a high-dose group (n=5, 5×10⁵ PFU). The mice were anesthetized by isoflurane inhalation and then infected via intranasal instillation. The mental state of the mice in each group was observed daily, and the body weight and mortality were recorded. On day 13 post-infection, 2% Evans Blue (4 mL/kg body weight) was administered via tail vein injection to assess blood-brain barrier (BBB) disruption. Subsequently, brain tissue samples were collected for immunofluorescence analysis to evaluate the activation of astrocytes and microglia. ResultsThe titer of purified VTT was 1×10⁷ PFU/mL. Compared with the control group, mice in the low-dose group showed no significant change in body weight, and no lethality was observed. In contrast, mice in the high-dose group exhibited significant weight loss starting on day 5 post-infection (P<0.05), accompanied by lethality. On day 13 post-infection, no Evans Blue extravasation was detected in the brain tissues of the low-dose group, while the olfactory bulb region of the high-dose group displayed distinct blue staining, indicating disruption of the BBB. Immunofluorescence analysis revealed no significant proliferation of astrocytes and microglia in the olfactory bulb region of the low-dose group on day 13 post-infection. In contrast, marked activation of glial cells was observable in the high-dose group. ConclusionAn animal model of VTT-induced encephalopathy in AG6 mice is successfully established, characterized by BBB disruption and reactive gliosis specifically localized to the olfactory bulb region, manifested as astrocytic and microglial proliferation.

Background Prehospital emergency medical personnel (PEMP) are exposed to long-term high-pressure work, which can exacerbate psychological distress and impair job satisfaction and sleep quality. However, in-depth research on the interactions among these factors is lacking. Objective To assess the status of psychological distress, job satisfaction, and sleep quality of PEMP in Guangzhou and to explore the mediating role of sleep quality in the relationship between psychological distress and job satisfaction. Methods From February to May 2025, 1085 PEMP from "120" emergency network hospitals in Guangzhou were selected using convenience sampling. Data were collected via the General Information Questionnaire, Kessler Psychological Distress Scale, Minnesota Satisfaction Questionnaire, and Pittsburgh Sleep Quality Index. Statistical analyses were performed using SPSS 25.0, and The mediation model of sleep quality in linking psychological distress and job satisfaction was constructed using AMOS 28.0. The bias-corrected Bootstrap method was employed to assessed the significance of the mediating effect. Results A total of 1063 valid responses were received (97.97% valid response rate). The mean scores were: psychological distress (27.99±10.75), job satisfaction (69.45±15.84), and sleep quality (9.82±4.47). Significant differences in the three scores were found across gender, age, monthly night shift frequency, and hospital grade (P<0.05). Higher job satisfaction was linked to lower psychological distress and better sleep quality and its dimensions, while psychological distress directly correlated with poorer sleep quality (P<0.01). Sleep quality partially mediated the relationship between psychological distress and job satisfaction, with a mediating effect of −0.195, accounting for 43.62% of the total effect. Conclusion The participants report moderate psychological distress, moderate-to-high job satisfaction, and poor sleep quality. Psychological distress directly affects job satisfaction and indirectly through its impact on sleep quality. Interventions aimed at improving sleep health and mental health are essential to improve personnel well-being and work efficiency.

Background Prehospital emergency medical personnel (PEMP) are exposed to long-term high-pressure work, which can exacerbate psychological distress and impair job satisfaction and sleep quality. However, in-depth research on the interactions among these factors is lacking. Objective To assess the status of psychological distress, job satisfaction, and sleep quality of PEMP in Guangzhou and to explore the mediating role of sleep quality in the relationship between psychological distress and job satisfaction. Methods From February to May 2025, 1085 PEMP from "120" emergency network hospitals in Guangzhou were selected using convenience sampling. Data were collected via the General Information Questionnaire, Kessler Psychological Distress Scale, Minnesota Satisfaction Questionnaire, and Pittsburgh Sleep Quality Index. Statistical analyses were performed using SPSS 25.0, and The mediation model of sleep quality in linking psychological distress and job satisfaction was constructed using AMOS 28.0. The bias-corrected Bootstrap method was employed to assessed the significance of the mediating effect. Results A total of 1063 valid responses were received (97.97% valid response rate). The mean scores were: psychological distress (27.99±10.75), job satisfaction (69.45±15.84), and sleep quality (9.82±4.47). Significant differences in the three scores were found across gender, age, monthly night shift frequency, and hospital grade (P<0.05). Higher job satisfaction was linked to lower psychological distress and better sleep quality and its dimensions, while psychological distress directly correlated with poorer sleep quality (P<0.01). Sleep quality partially mediated the relationship between psychological distress and job satisfaction, with a mediating effect of −0.195, accounting for 43.62% of the total effect. Conclusion The participants report moderate psychological distress, moderate-to-high job satisfaction, and poor sleep quality. Psychological distress directly affects job satisfaction and indirectly through its impact on sleep quality. Interventions aimed at improving sleep health and mental health are essential to improve personnel well-being and work efficiency.

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

Objective:To evaluate the differences in total effectiveness, Visual Analogue Scale (VAS) scores, and Cox Menstrual Symptom Scale (CMSS) scores among four non-invasive Traditional Chinese Medicine (TCM) techniques: moxibustion, massage therapy, acupoint application, and auricular acupressure, for the care of primary dysmenorrhea (PD) using network Meta-analysis, providing evidence-based guidance for clinical decision-making.Methods:Randomized controlled trials (RCTs) involving the four non-invasive TCM techniques for PD care were retrieved from databases including China National Knowledge Infrastructure, VIP, Wanfang, SinoMed, PubMed, Embase, Web of Science, and Cochrane Library up to November 30, 2023. Network Meta-analysis was conducted using RevMan 5.4 and Stata 17.0.Results:A total of 83 studies involving 8 370 PD patients were included. The network Meta-analysis showed that all four TCM techniques were superior to control measures in terms of total effectiveness and reducing VAS scores. Moxibustion, acupoint application, and auricular acupressure were also superior in reducing CMSS scores. Ranking of the effects of different interventions: total effectiveness: auricular acupressure > moxibustion > acupoint application > massage therapy > acupuncture > Chinese patent medicine > Western medicine > blank control > placebo treatment; VAS scores: moxibustion > auricular acupressure > acupoint application > acupuncture > placebo treatment > massage therapy > Western medicine > Chinese patent medicine > blank control; CMSS scores: auricular acupressure > moxibustion > acupuncture > placebo treatment > acupoint application > Western medicine > blank control.Conclusions:The four non-invasive TCM techniques have significant advantages in PD care, with auricular acupressure and moxibustion being the preferred choices for clinical care.

Objective To evaluate the efficacy and safety of enteric-coated metformin hydrochloride capsules(Junlida?)in patients with T2DM and poor glycemic control under lifestyle interventions.Methods In this study,419 patients with T2DM were recruited from 15 research centers from July 2020 to March 2022,and randomly divided into observation(Obs)group(n=209)and control group(Con,n=210)using a multicenter,randomized,double-blind,non-inferiority trial design.Patients in the Obs group were treated with enteric-coated Metformin hydrochloride capsules(Junlida?),and patients in the Con group were treated with Metformin hydrochloride tablets(Glucophage?).The optimal effective dose of 2 g/d was achieved within 4 weeks,and the reasonable dose was maintained until the end of treatment.The treatment period was 24 weeks.HbA1c and its compliance rate,FPG,and body weight were compared between the two groups in full analysis set(FAS)and protocol set(PPS).Safety and adverse events(AE)were evaluated in safety set(SS).Results A total of 414 participants were randomized(207 cases in Obs group and 207 cases in Con group).414 cases in FAS population(207 cases in Obs group and 207 cases in Con group),and 328 cases in PPS population(164 cases in Obs group and 164 cases in Con group),and 414 cases in SS population(207 cases in Obs group and 207 cases in Con group).After treatment,HbA1c,FPG and body weight were lower in both groups(P<0.05)in FAS and PPS.HbA1c compliance rate was not significantly different between the two groups in FAS and PPS(P>0.05).The results of non-inferiority test showed that the lower limit was>-0.4%in both FAS(-0.154,95%CI-0.384～0.069)and PPS(-0.139,95%CI-0.390～0.112),and the Obs group reached non-inferiority end point.The achievement rate,compliance rate,safety index and incidence of AE were not significantly different between the two groups(P>0.05).Conclusions Junlida? demonstrated non-inferiority to Glucophage? in glycemic control and can be safely and effectively used in patients with diabetes.

Objective To explore the value of CT-based radiomics and machine learning in predicting the severity of community acquired pneumonia(CAP)in children.Methods The clinical and imaging data of 158 patients diagnosed with CAP in children were analyzed retrospectively.All patients were randomly divided into training set(n=110)and validation set(n=48)in a ratio of 7︰3.Radiomics features were outlined and extracted using 3D Slicer software,and feature selection was achieved using maximum relevance and minimum redundancy(MRMR)and least absolute shrinkage and selection operator(LASSO)algorithms.The construction of the nomogram model and the machine learning combined model was performed by combining clinical features and Radiomics score(Radscore),and its performance was evaluated and validated.Results The area under the curve(AUC)of the clinical model,the radiomics model and the nomogram model in the validation set were classified as 0.82,0.86 and 0.91,respectively.The AUC of the combined multi-layer perceptron(MLP),random forest(RF),and adaptive boosting(ADB)models were 0.926,0.934 and 0.917,respectively in the validation set.Conclusion Radiomics combined with clinical data is expected to be a novel predictor of the severity of CAP in children.MLP,RF and ABD machine learning algorithms can further enable model performance.

Objective To construct mouse models of vaccinia virus infection using C57BL/6N and BALB/C mice infected with vaccinia virus VR-1354(WR(NIH TC-adapted)),and to compare the differences between the models in the two mouse strains.Methods Vaccinia virus VR-1354 was amplified,concentrated,and titrated,according to conventional method.The median lethal doses(LD50)of vaccinia virus V R-1354 for C57BL/6N and BALB/C mice were determined,respectively,and mice from the two strains were infected with the respective LD50.Lung tissues from control and experimental mice were removed for hematoxylin and eosin staining on days 3,7,and 14,and changes in the virus load in the lung tissues were measured at the same times.Results The titer of the amplified vaccinia virus VR-1354 was 3×108 plaque forming units(PFU)/mL.The LD50 values in C57BL/6N and BALB/C mice were 2.5×103and BALB/C 2.8×103PFU,respectively.After infecting mice from both strains with 2.5× 103 PFU of vaccinia virus VR 1354,lung tissue lesions worsened with increasing infection time,but the symptoms of the mice tended to recover after day 7.The virus loads in lung tissues began to increase on day 3 in both strains,peaked on day 7,and then gradually decreased.Conclusions C57BL/6N and BALB/C mouse models infected with vaccinia virus VR-1354 were established successfully.The virus can cause obvious lesions in mouse lung tissues,including inflammatory cell infiltration and alveolar cavity fluid exudation.The degree of disease onset and susceptibility differed between the two mouse strains,with C57BL/6N mice showing higher susceptibility to this virus than BALB/C mice.

Pancreaticoduodenectomy (PD) is a primary surgical approach for treating mali-gnant tumors of the pancreatic head and the periampullary region. With the advance in medical technology in recent years, the long-term survival rate of patients undergoing PD has significantly improved, and the incidence of early perioperative complications has markedly decreased. However, current researches predominantly focuse on early postoperative complications, while, limited studies addressing long-term complications. Long-term complications after PD have a significant impact on patients′ quality of life and long-term survival. This authors systematically summarize the common long-term complications following PD, and explore their mechanisms, clinical manifestations, dia-gnostic methods, and treatment strategies, aiming to provide a reference for clinical practice.

Objective To evaluate the modeling characteristics and predictive performance of models for predicting post-stroke neurological deterioration(ND)published in existing literatures.Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidance for scoping reviews,a comprehensive search was conducted in PubMed,CINAHL,Cochrane Library,Embase,Web of Science,Scopus,CNKI,Wanfang Data,and VIP databases from inception to December 15,2024.The search strategy combined Medical Subject Headings(MeSH)and free-text terms,with key words including"Stroke""Ischemic Stroke""Neurological Deterioration""Nomograms""Risk Prediction""Predictive Models""卒中""脑梗死""脑出血""神经功能恶化"and"预测模型".Base on the data extraction checklist and critical appraisal,data extraction covered three domains:(1)basic characteristics,including author,publication year,country,study design(retrospective,prospective,registry-based),sample source(single-center,multicenter),stroke subtypes(acute ischemic stroke[AIS]-conservative therapy,AIS-intravenous thrombolysis[IVT],AIS-endovascular therapy[EVT],intracerebral hemorrhage[ICH]),ND time windows(acute[≤72 h],subacute[≤ 7 d],long-term[≤90 d]),and outcome types(single/composite endpoints);(2)model evaluation metrics,including missing data handling(complete-case analysis,multiple imputation),model development methodologies(multivariate Logistic regression,least absolute shrinkage and selection operator regression,machine learning),presentation formats(nomograms,web calculators,risk prediction tool),discrimination(area under the curve,C-index),calibration(Hosmer-Lemeshow test,calibration curve and slope),clinical utility(decision curve analysis[DCA],global metrics Brier score,R2,AIC),sample size(training set,internal validation set,external validation set),sample size requirements(events per variable[EPV]≥10 to mitigate overfitting),and validation(internal/external);(3)predictor features,including selection strategies(prior knowledge-driven,univariate analysis),quantity,and attributes(demographics,medical history,physical examination,treatment intervention information,imaging/laboratory indicators).Predictive models that meet exclusion criteria from prior literature were analyzed by their discrimination,calibration,clinical utility and global metrics.Forest plots were utilized to visualize discrimination(evaluated via difference in area under the curve)of the extracted models.The prediction model risk of bias assessment tool(PROBAST)was applied to assess bias risk and clinical applicability.Occurrence frequencies of the post-stroke neurological deterioration predictors were ranked and the top 6 high-frequency predictors were extracted.Results(1)Among 3 728 screened studies,25 were included based on the inclusion and exclusion criteria.(2)Basic characteristics:retrospective(72％[18/25])and single-center(64％[16/25])designs dominated.With most models targeted on AIS(92％[23/25]),and the rest(8％[2/25])on ICH.ND was primarily defined by neurological scale changes(60％[15/25];e.g.,National Institutes of Health stroke scale[NIHSS]score increase or Glasgow coma scale[GCS]score decrease),with time windows categorized as acute(36％[9/25]),subacute(48％[12/25]),or long-term(16％[4/25]).(3)Model evaluation:multivariate Logistic regression(96％[24/25])and nomograms(88％[22/25])were predominant.Only 24％(6/25)explicitly addressed missing data handling methods,and 52％(13/25)with EPV≥10.The median area under the curve was 0.865(range:0.650-0.981).44％(11/25)of the studies reported calibration curves,and 4％(1/25)reported calibration slopes.All studies utilized DCA to validate their clinical applicability,84％(21/25)of the studies conducted internal validation,while only 32％(8/25)conducted external validation.PROBAST evaluation revealed low overall bias risk in 8％(2/25;no error across participant,predictor,outcome,or analysis domains)and low clinical applicability risk in 44％(11/25;alignment with target populations,accessible predictors,and clinically relevant outcomes)of the studies.(4)Predictors:64％(16/25)of the predictor were screened predominantly through the prior knowledge-driven based strategy.The top 6 high-frequency predictors are NIHSS score(64％[16/25]),age(36％[9/25]),blood glucose/diabetes(36％[9/25]),blood pressure/hypertension(32％[8/25]),the Alberta stroke program early CT score(20％[5/25]),and neutrophil-to-lymphocyte ratio(20％[5/25]).AIS-ND predictors emphasized readily available metrics,such as NIHSS(65％[15/23]),age(35％[8/23]),while ICH-ND primarily relied on imaging markers(e.g.,baseline hematoma volume[2/2],location[1/2]).Conclusion Current post-stroke ND predictive models demonstrate satisfactory performance on discrimination and multimodal integration,but their practical application are hindered by insufficient calibration quantification,high bias risk,and limited clinical translatability.