Objective A randomized controlled trial was conducted on colonoscopy inpatient and outpatients to compare the efficacy and safety of water exchange(WE)colonoscopy and CO2 convention insufflation colonoscopy in elderly patients.Methods 340 patients underwent fully sedated colonoscopy were randomly divided into two groups according to colonoscopy with either WE colonoscopy group(WE group)and CO2 insufflation colonoscopy group(CO2 group).The two groups were compared in terms of Boston bowel preparation scale(BBPS),withdrawal time,cecal intubation time,cecal intubation success rate,abdominal compression,willingness to repeat,polypdetectionrate(PDR),adenoma detection rate(ADR),and safety.Results The cecal intubation success rate was significantly higher in WE group(100.0%)compared with CO2 group(96.5%),the difference was statistically significant(P=0.013).The average cecal intubation time of WE group was(10.50±1.79)min,which was longer than that of CO2 group(7.55±1.50)min,and the difference was statistically significant(P<0.01).Comparison of withdrawal time and BBPS between the two groups,the differences were not statistically significant(P>0.05).The abdominal pressure rate was lower in WE group(5.9%)compared with CO2 group(13.5%),the difference was statistically significant(P=0.017).The rate of willingness to re-examination in the WE group was 98.2%,which was significantly higher than the 93.5%in the CO2 group.The PDR in WE group(80.6%)was higher than that in CO2 group(70.6%),the ADR in WE group(67.1%)was higher than that in CO2 group(50.6%),the differences were statistically significant(P<0.05).Multivariate Logistic regression analysis showed that WE group was an effective factor in improving ADR(O＾R=2.027,P<0.01).The overall adverse events were less than 3%,with no difference between the two groups(P=1.000).Conclusion The use of WE colonoscopy has a better improved efficacy in elderly patients,and safety should be ensured by individualized assessment of the patient's co-morbidities,bowel preparation tolerance,and willingness prior to the procedure.

Objective:To explore the incidence of delirium in patients of advanced age hospitalized in internal medicine departments and analyze its influencing factors.Methods:A retrospective analysis was conducted on the medical records of 586 patients of advanced age hospitalized in internal medicine departments at the Beijing University of Chinese Medicine Third Affiliated Hospital from May 2023 to May 2024. Patients were divided into a delirium group and a non-delirium group based on whether delirium occurred. Univariate analysis and binary Logistic regression analysis were used to explore the factors influencing delirium in patients of advanced age hospitalized in internal medicine departments.Results:Among 586 patients of advanced age hospitalized in internal medicine departments, the incidence of delirium was 21.2% (124/586). Binary Logistic regression analysis showed that age, activities of daily living (Barthel Index), folate deficiency, sleep disorders, and indwelling catheters were factors influencing delirium in patients of advanced age hospitalized in internal medicine departments ( P<0.05) . Conclusions:The incidence of delirium is high among patients of advanced age hospitalized in internal medicine departments. Healthcare professionals should pay particular attention to elderly patients with advanced age, limited activities of daily living, folate deficiency, sleep disorders, and indwelling catheters, and should implement targeted preventive strategies as early as possible.

Objective:To develop the Sense of Gain from Ideological and Political Elements in Nursing Undergraduate Practice Courses Scale and to examine its reliability and validity.Methods:From December 2023 to March 2025, guided by the theoretical framework of ideological and political education gain, and considering the characteristics of nursing practice courses, the scale was preliminarily developed through literature review, focus group interviews, and expert consultations. From April to May 2025, a convenience sample of 368 nursing undergraduate interns from seven affiliated teaching hospitals of Guangzhou Medical University was surveyed to test the psychometric properties of the scale.Results:The finalized scale contained 4 dimensions and 13 items. Exploratory factor analysis revealed that the four factors explained 76.359% of the total variance. Confirmatory factor analysis indicated good model fit indices. The scale-level content validity index ( S- CVI) was 0.964, and the item-level content validity index ( I- CVI) ranged from 0.875 to 1.000. The total Cronbach's α coefficient of the scale was 0.855, and the split-half reliability coefficient was 0.717. Conclusions:The Sense of Gain from Ideological and Political Elements in Nursing Undergraduate Practice Courses Scale demonstrates good reliability and validity. It can serve as a valid and reliable instrument for nursing educators and administrators to evaluate and monitor the quality of ideological and political education in nursing practice courses.

Objective To analyze challenges in translating exercise management evidence for patients with metabolism-associated fatty liver disease(MAFLD),develop actionable strategies,and evaluate the application of best evidence.Methods Utilizing the evidence translation model,the best evidence was implemented for MAFLD patients in 4 phases:evidence acquisition,baseline practice review,intervention,and outcome evaluation.We compared the knowledge of exercise management evidence,implementation rates of review indicators,completion of exercise programs,BMI,liver stiffness measurement,controlled attenuation parameters,and patient satisfaction among medical staff at a tertiary hospital in Fujian Province during baseline(March-May 2023),mid-practice(June-August 2023),and late-practice(September-November 2023)phases.Results A total of 88 patients were included at baseline review,95 during mid-practice,and 107 in late-practice.Significant improvements were observed in the implementation rates of 21 review indicators,nurses'knowledge,completion rate,BMI,and controlled attenuation parameters compared to the data at baseline(P＜0.05).Conclusion The application of best evidence in exercise management for MAFLD patients enhances nurses'knowledge,standardizes nursing practices,and reduces patients'BMI and controlled attenuation parameters.

Objective:To investigate the clinicopathological characteristics and differential diagnosis of DICER1-mutant primary intracranial sarcoma.Methods:Five cases of DICER1-mutant primary intracranial sarcoma at Sanbo Brain Hospital, Capital Medical University, Beijing, China during May 2013 to November 2024 were collected. The clinical and imaging data were retrieved. Histological evaluation, immunohistochemical staining and next generation sequencing were performed. Additionally, a literature review was conducted.Results:All five DICER1-mutant primary intracranial sarcomas were located in the supratentorial region, with one case involving the basal ganglia. There were two males and three females. The median age at diagnosis was 25 (14.0, 30.5) years. Morphologically, they were characterized by high-grade spindle cell sarcoma, with brisk mitotic activity and cytoplasmic eosinophilic globules. Myxoid degeneration, necrosis, and invasion into surrounding brain tissue were observed in some cases. The tumor cells showed diffuse staining of vimentin and variable expression of myogenic marker (desmin), with or without focal MyoD1 and/or Myogenin expression. Four tumors exhibited diffuse, strong expression of TLE1 and p53, while only three tumors showed loss of ATRX (nuclear) expression. Two cases showed mosaic loss of H3K27me3 expression in neoplastic cells. The Ki-67 proliferation index was high (40%-80%). Various neuronal markers, such as synaptophysin, NF, SOX2 and MAP2, were expressed in all tumor samples. Genetically, all tumors samples harbored biallelic abnormalities of DICER1. One was a hotspot missense mutation in the RNase Ⅲb domain within exon 25 on one allele (p.E1813 or p.D1810), while the other allele had mutations including a germline mutation in one case, a somatic mutation in two cases, and a copy number deletion in two cases. In addition, these sarcomas showed alterations in TP53 (4/5), ATRX (3/5), and the genes of the mitogen-activated protein kinase pathway (3/5). Finally, all five cases were diagnosed as DICER1-mutant primary intracranial sarcoma. All patients underwent craniotomy that led to complete tumor resection. Three patients received adjuvant radiotherapy and chemotherapy, with progression-free survival time of 28, 48, and 50 months, respectively. Patient 2 succumbed to the tumor after 3 months post-surgery due to rapid progression and tumor dissemination. Patient 5 was lost to follow-up 3 months after the surgery.Conclusions:DICER1-mutant primary intracranial sarcoma is a newly defined tumor entity in the fifth edition of the World Health Organization Classification of Central Nervous System Tumors, and commonly occurs in children and young adults. High-grade malignant spindle cells are their typical morphological feature. Eosinophilic cytoplasmic globules and myogenic differentiation can help establish the diagnosis. This study suggests that DICER1-mutant primary intracranial sarcomas exhibit immunophenotypic neuronal differentiation. Rendering the diagnosis of DICER1-mutant primary intracranial sarcoma largely relies on detecting DICER1 pathogenic alterations or DNA methylation profiling.

Objective To evaluate the diagnostic value of positive serum specific antibodies to Mycoplasma pneumoniae(MP)in children with Mycoplasma pneumoniae pneumonia(MPP).Methods PubMed,Cochrane Library,Embase,Sinomed,CNKI,Wanfang and VIP databases were searched for studies on the detection of MPP based on antibodies from the establishment of the database to March 31,2023.After literature screening and data extraction,STATA 16.0 software was used for Meta-analysis.Results A total of 9 literatures and 2 148 clinical samples were included.The combined sensitivities[M(95%CI)]of particle agglutination assay(PA)and enzyme-linked immunosorbent assay(ELISA)were 50%(31～69)and 88%(85～90),and the combined specificities[M(95%CI)]were 88%(76～95)and 88%(62～97).The combined diagnostic odds ratio(DOR)[M(95%CI)]were 5.61(3.30～9.53)and 43.82(12.78～150.19),and the summary receiver operating characteristic curve(SROC)area under the curve(AUC)were 0.80 and 0.88,respectively.Conclusion Serum MP specific antibody detection can be used for diagnosis and screening of children MPP,but needs to be combined with clinical symptoms improve the accuracy of the diagnosis.

Objective:To evaluate the effect of an obstetric artificial intelligence (AI) assistant combined with a family-centered health education model on maternal self-care ability, comfort status, and spousal caregiving ability.Methods:This prospective, single-center, parallel randomized controlled trial used 1∶1 randomization and was conducted as a superiority trial. Postpartum mothers and their spouses admitted to family-style single rooms at the Third Affiliated Hospital of Guangzhou Medical University between October 2024 and April 2025 were enrolled and randomly assigned to control or intervention groups using a random number table. The control group received conventional health education, while the intervention group received conventional health education plus the AI-assisted family-centered model. Interventions were administered at 2 hours, 6 hours, and 24 hours postpartum, and before discharge. Outcomes included maternal self-care ability, comfort status, and spousal caregiving ability, which were assessed at 2 hours postpartum and before discharge. Data were analyzed using independent and paired t-tests and Chi square tests. Results:Of the 88 mother-spouse dyads initially recruited, four were excluded due to mother-infant separation (e.g., neonatal jaundice), leaving 84 dyads (42 per group). After the intervention, the intervention group showed significantly higher maternal self-care ability scores [(192.81±13.80) vs. (181.00±21.41) scores, t=3.00], higher maternal comfort scores [(104.43±7.52) vs. (96.00±14.29) scores, t=3.38], and better spousal caregiving ability [(6.07±3.13) vs. (9.50±5.02) scores, t=－3.76] compared to the control group (all P<0.05). Conclusion:The obstetric AI assistant combined with a family-centered health education model significantly improved maternal self-care ability and comfort status, as well as spousal caregiving ability.

Sepsis-associated encephalopathy (SAE) is one of the complications of sepsis, causes cognitive dysfunction ranging from mild attention deficits to progression into coma, which severely impairs patients' ability to live and mental health, and increases the long-term disability and mortality rates. Although the clinical attention to SAE has been increasing in recent years, effective interventions to improve cognitive dysfunction in sepsis survivors are still in the preclinical stage. The pathogenesis of SAE is numerous and complex, and mitochondrial dysfunction, as one of the key pathogenic mechanisms, plays a role in the cognitive development process through oxidative stress imbalance, energy metabolism disorders, and activation of apoptosis signaling pathway. The present review systematically integrates the recent studies on mitochondrial dysfunction in the development of cognitive disorders. This review systematically integrates the cutting-edge research results in recent years, discusses the mitochondrial structural disruption, mitochondrial kinetic abnormalities, respiratory chain dysfunction, and comprehensively comprehends the research progress of mitochondria-targeted antioxidant, mitochondrial autophagy activator, mitochondrial biosynthesis modifier and other novel intervention strategies in improving cognitive function of SAE patients, with the aim of providing theoretical basis for the breakthrough of the current status of clinical treatment of SAE and the targeting of mitochondria for treatment. The aim is to provide theoretical basis for breaking through the status of SAE clinical treatment and targeting mitochondrial therapy.
Humans ; Sepsis-Associated Encephalopathy/metabolism* ; Mitochondria/metabolism* ; Sepsis/complications* ; Oxidative Stress ; Cognitive Dysfunction ; Autophagy

Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

Objective In this study,clinical data of Alzheimer's disease(AD)patients,structural magnetic resonance imaging(sMRI)data,and positron emission tomography(PET)data were used to construct an auxiliary diagnostic model with good classification effects,so as to formulate a personalized treatment plan at the early stage of the patients,which is of great significance for the prevention and treatment of AD.Methods In this study,a total of 401 study subjects containing complete sMRI images and PET images were selected from the ADNI-1(Alzheimer's disease neuroimaging initiative-1,ADNI-1)database.We used statistical parameters mapping(SPM)and voxel-based morphometric(VBM)analysis of MATLAB to perform pre-processing operations such as spatial normalization and skull stripping on sMRI images and PET images of the study subjects.With the help of the brain atlas was used to segment the brain tissue structure.After that,the segmented gray matter was extracted from the corresponding brain regions based on anatomical automatic labeling,and the feature values of all brain regions were obtained.Then the extracted brain region feature values are then subjected to fisher score,support vector machine-recursive feature elimination(SVM-RFE)and least absolute shrinkage and selection operator(LASSO),a hybrid filtered-wrapped-embedded feature selection method with three different principles,to realize the dimensionality reduction of high-dimensional image data.Finally,the PAC-Bayesian strategy boosting based multi-view learning(PB-MVBoost)model is constructed based on multi-view decision fusion for clinical,sMRI and PET data.And it is compared with the traditional machine learning models support vector machine(SVM),decision tree(DT),K-nearest neighbor(KNN),random forests(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)which are constructed after concatenating views.It is compared with multi-view multi-kernel learning models(AverageMKL,EasyMKL)and multi-view confusion matrix boosting,which is also the same multi-view decision fusion.Results Among all the multi-view fusion models of AD-MCI,the PB-MVBoost model based on decision fusion has the best performance(accuracy=0.98,F1-score=0.97,precision=0.98,recall=0.96,MSE=0.07).Among all the multi-view fusion models of MCI-NC,the model performance of PB-MVBoost based on decision fusion was the best(accuracy=0.99,F1-score=0.98,precision=0.99,recall=0.98,MSE=0.05).Conclusion In the classification of AD-MCI and MCI-NC,the distinction and calibration degree of PB-MVBoost model were optimized,indicating that the auxiliary diagnosis model of Alzheimer's disease constructed by PB-MVBoost classifier based on decision fusion performed the best,which could improve the recognition of patients with mild cognitive impairment and then assist clinical diagnosis.