Functional constipation （FC） is a clinically common functional bowel disorder characterized by a protracted course and associations with various chronic disorders and psychological abnormalities. Although not life-threatening， FC significantly impairs patients' quality of life. FC subtypes include slow-transit constipation （STC）， defecatory disorder （DD）， and normal-transit constipation （NTC）. The pathological mechanisms underlying FC have not been fully elucidated， and overall clinical efficacy remains unsatisfactory. Animal models of FC serve as essential tools for the study of disease mechanisms and the development of novel therapeutics. This article systematically reviews the current state of research on the animal models of FC and identifies that rodents， particularly rats and mice， are the most commonly used species. Dogs and pigs are also employed in complex intervention studies due to their physiological similarities to humans， though their use is limited by housing challenges and ethical considerations. Induction methods vary across different FC subtypes. STC models are primarily established with chemical agents such as loperamide or compound diphenoxylate. DD modeling often involves low-fiber diets combined with methylene blue injection or rectal narrowing. NTC modeling mainly relies on low-fiber dietary interventions. In addition， disease-syndrome combination models based on traditional Chinese medicine （TCM） theory have been developed， encompassing excess patterns such as heat accumulation， cold accumulation， and Qi stagnation， as well as deficiency patterns including Qi deficiency， blood deficiency， Yin deficiency， and Yang deficiency. These are achieved through an approach of disease model + syndrome induction， enabling the integration of mechanisms from both Western and TCM perspectives. Models are evaluated from two aspects： disease and syndrome manifestations （e.g.， colonic transit， secretory function， and TCM syndrome indicators such as mental state and body weight） and disease mechanisms （e.g.， enteric nervous system， interstitial cells of Cajal， smooth muscle cells， gut microbiota， and metabolites）. However， current research still faces challenges such as poor consistency in some models， non-specific interference in mechanism interpretation， insufficient studies on NTC， and lack of TCM tongue and pulse diagnosis in evaluation. Future efforts should focus on optimizing model stability and specificity to provide a more reliable experimental basis for investigating the pathological mechanisms of FC and developing therapeutic agents.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To investigate the effects of long non-coding RNA FGD5-AS1 on the proliferation,migration,and invasion of pituitary adenoma(PA)cells,and to analyze its potential mechanism of action.Methods Human PA cell lines HPAs,RC-4BC,HP75,and human astrocyte cell line NHA were cultured in vitro.The expression levels of FGD5-AS1 and miR-15a in the above cell lines were detected by RT-PCR.HP75 cells in the logarithmic growth phase were randomly divided into the silencing group and the negative control group.The silencing group was transfected with shRNA-FGD5-AS1,while the negative control group was transfected with shRNA-NC.The expression levels of FGD5-AS1 and miR-15a in the two groups of cells were detected by RT-PCR.The proliferation,migration and invasion abilities of the two groups of cells were determined by CCK-8 assay,wound healing assay,and Transwell assay.The expression of proteins related to the Wnt/β-catenin signaling pathway in the two groups of cells was detected by Western blot.The targeting relationship between FGD5-AS1 and miR-15a was verified by dual-luciferase reporter gene assay.Results Compared with the NHA cell,the expression level of FGD5-AS1 was significantly increased in the HPAs,RC-4BC,and HP75 cells((P<0.05),whereas the expression level of miR-15a was significantly decreased(P<0.05).Compared with the negative control group,the expression level of FGD5-AS1 was decreased(P<0.05),the expression level of miR-15a was increased(P<0.05),the OD value was decreased(P<0.05),the migration and invasion abilities of cells were reduced(P<0.05),and the expression of Wnt3a and β-catenin proteins was decreased in the silencing group of HP75 cells(P<0.05).FGD5-AS1 could specifically bind to miR-15a,leading to a decrease in cell luciferase activity(P<0.05).Conclusion FGD5-AS1 is overexpressed in PA cells,and silencing FGD5-AS1 can inhibit the proliferation,migration,and invasion of PA cells,and the mechanism is related to its targeted regulation of miR-15a.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.

Objective:To analyze the clinical effect of health education based on Theory of Inventive Problem Solving(TRIZ)mode in patients with senile degenerative heart valvular disease(SDHVD).Methods:This randomized con-trolled study enrolled 126 SDHVD patients admitted in Department of Cardiology,The First Affiliated Hospital of Xinjiang Medical University between May 2020 and October 2022.They were divided into control group(n=63,health education mode)and intervention group(n=63,health education based on TRIZ mode),and intervened for 3 months.Coagulation function,cardiac function indexes,mastery of health knowledge,incidence of adverse cardi-ovascular events,compliance of medication and review were compared between two groups.Results:After inter-vention,compared with patients in control group,those in intervention group had significant higher activated partial thromboplastin time(APTT)[(39.22±2.04)s vs.(32.82±1.73)s],prothrombin time(PT)[(14.32±0.39)s vs.(12.73±0.45)s],left ventricular ejection fraction(LVEF)[(58.58±1.87)％vs.(53.58±1.48)％],scores of health knowledge questionnaire[(78.95±2.27)points vs.(55.07±3.63)points]and Morisky Medication Ad-herence Scale-8[(8.27±0.52)points vs.(6.28±0.89)points]and proportion of review(93.65％vs.65.08％),and significant lower left ventricular end-diastolic diameter(LVEDd)[(51.70±2.12)mm vs.(57.61±1.56)mm]and total incidence of adverse cardiovascular events(15.87％vs.36.51％)(P＜0.01 all).Conclu-sion:The health education mode based on TRIZ theory could effectively improve coagulation function and cardiac function,improve patients'understanding of the disease,medication and review compliance,and reduce incidence of adverse cardiovascular events in SDHVD patients.

Objective:To investigate the predictive value of serum neutrophil gelatinase-associated lipocalin(NGAL),calcitonin gene-related peptide(CGRP),and neutrophil-to-lymphocyte ratio(NLR)for the prognostic regression of elderly patients with stroke complicated with pulmonary infection(SCPI).Methods:This study was a retrospective single-center study,149 elderly patients with SCPI who were admitted to Inner Mongolia Baogang Hospital from June 2020 to June 2024 were selected,they were divided into poor prognosis group(n=56)and good prognosis group(n=93)according to the prognosis.Baseline data and laboratory test indicators were collected,and NLR was calculated.Serum NGAL and CGRP levels were measured by ELISA.Influencing factors of poor prognosis of elderly patients with SCPI were analyzed by Multivariate logistic regression.Predicts value was analyzed by Receiver operating characteristic(ROC)curve.Results:Compared with good prognosis group,the poor prognosis group had higher of aged ≥ 70 years,incidence of hemorrhagic stroke,serum creatinine,white blood cell count,national institute of health stroke scale(NIHSS),platelet count,C-reactive protein,NGAL,and NLR levels,longer nerosurgery intensive care unit(NICU)stay,and lower CGRP levels(P＜0.05).Higher CGRP level was an independent protective factor of poor prognosis of elderly patients with SCPI(OR＜1,P＜0.05).Age ≥ 70 years,hemorrhagic stroke,longer NICU stay,higher NIHSS score,higher NGAL level and higher NLR were independent risk factors of poor prognosis of elderly patients with SCPI(OR＞1,P＜0.05).The area under the curve(AUC)for predicting the prognostic regression of elderly patients with SCPI used NGAL,CGRP,and NLR alone or in combination was 0.777,0.771,0.786,and 0.927,respectively,with the combination of three factors showed the highest predictive power(P＜0.05).Conclusion:Age ≥70 years,hemorrhagic stroke,longer NICU stay,higher NIHSS score,higher NGAL level and higher NLR are independent risk factors of poor prognosis of elderly patients with SCPI,while higher CGRP level is an independent protective factor.The combination detection of NGAL,CGRP and NLR can improve the predictive value of prognostic regression in elderly patients with SCPI.

Objective:To investigate the predictive value of serum neutrophil gelatinase-associated lipocalin(NGAL),calcitonin gene-related peptide(CGRP),and neutrophil-to-lymphocyte ratio(NLR)for the prognostic regression of elderly patients with stroke complicated with pulmonary infection(SCPI).Methods:This study was a retrospective single-center study,149 elderly patients with SCPI who were admitted to Inner Mongolia Baogang Hospital from June 2020 to June 2024 were selected,they were divided into poor prognosis group(n=56)and good prognosis group(n=93)according to the prognosis.Baseline data and laboratory test indicators were collected,and NLR was calculated.Serum NGAL and CGRP levels were measured by ELISA.Influencing factors of poor prognosis of elderly patients with SCPI were analyzed by Multivariate logistic regression.Predicts value was analyzed by Receiver operating characteristic(ROC)curve.Results:Compared with good prognosis group,the poor prognosis group had higher of aged ≥ 70 years,incidence of hemorrhagic stroke,serum creatinine,white blood cell count,national institute of health stroke scale(NIHSS),platelet count,C-reactive protein,NGAL,and NLR levels,longer nerosurgery intensive care unit(NICU)stay,and lower CGRP levels(P＜0.05).Higher CGRP level was an independent protective factor of poor prognosis of elderly patients with SCPI(OR＜1,P＜0.05).Age ≥ 70 years,hemorrhagic stroke,longer NICU stay,higher NIHSS score,higher NGAL level and higher NLR were independent risk factors of poor prognosis of elderly patients with SCPI(OR＞1,P＜0.05).The area under the curve(AUC)for predicting the prognostic regression of elderly patients with SCPI used NGAL,CGRP,and NLR alone or in combination was 0.777,0.771,0.786,and 0.927,respectively,with the combination of three factors showed the highest predictive power(P＜0.05).Conclusion:Age ≥70 years,hemorrhagic stroke,longer NICU stay,higher NIHSS score,higher NGAL level and higher NLR are independent risk factors of poor prognosis of elderly patients with SCPI,while higher CGRP level is an independent protective factor.The combination detection of NGAL,CGRP and NLR can improve the predictive value of prognostic regression in elderly patients with SCPI.