Objective To explore the effectiveness of the generative large language model MedGo in nursing decision-making for elderly patients with multimorbidity. Methods A quasi-randomized controlled trial study was conducted involving 6 junior nurses, 6 senior nurses and the MedGo model from January 1, 2025 to March 31, 2025 at the Emergency Internal Medicine Ward of Shanghai East Hospital Affiliated to Tongji University. Clinical data of 120 elderly patients with multimorbidity were analyzed to compare the performance of the three groups in four tasks (nursing diagnosis assessment, nursing intervention formulation, complication identification, and complication prevention) from three evaluation dimensions: decision-making time consumption, decision accuracy, and decision-making quality. Results In terms of decision-making time, the senior nurse group completed all four tasks faster than the junior nurse group (P＜0.01), and the MedGo group completed all four tasks faster than the junior nurse group (P＜0.001) and the senior nurse group (P＜0.001). In terms of decision-making accuracy, senior nurse group scored higher than junior nurse group in all four tasks (P＜0.001), while the MedGo group outperformed the senior nurse group only in complication identification (P＜0.001). In terms of decision-making quality, the MedGo group scored higher than junior nurse group (P＜0.001) and senior nurse group (P＜0.001) in all four tasks. Conclusions The MedGo model demonstrates advantages of high efficiency, accuracy, and quality in nursing decision-making for elderly patients with multimorbidity; senior nurses outperform junior nurses in decision-making, providing diverse references for clinical nursing decision-making.

Objective To investigate the impact of red blood cell suspension infusion across various perioperative periods on patients with valvular heart disease. Methods The patients with valvular heart disease admitted to Tianjin Chest Hospital from 2018 to 2020 were selected. Based on the timing of perioperative red cell suspension infusion, patients were categorized into three groups: a group 1 receiving intraoperative red cell suspension infusion, a group 2 receiving red cell suspension infusion within 24 hours after entering the ICU, and a group 3 receiving red cell suspension infusion at both time points. The laboratory results, perioperative blood component infusion volume, and other relevant parameters were retrospectively analyzed. After propensity score matching, the differences in different variables among the three groups were compared. Results After propensity score matching, 102 patients were enrolled, including 52 males and 50 females, with an average age of (61.74±10.58) years. There were 34 patients in each group. The preoperative hemoglobin (Hb) value of the group 2 was significantly higher than that of the group 1 and the group 3, and the amount of red cell suspension and autoblood transfusion was the lowest (P<0.05). Group 1 had the highest postoperative Hb, as well as the highest Hb and hematocrit (HCT) levels within 24 hours post-surgery (P<0.05). The group 1 had the lowest plasma, platelet and cryoprecipitate infusion volumes, and the shortest cardiopulmonary bypass time, aortic occlusion time, postoperative ICU stay and hospital stay, and the least blood loss and total drainage volume (P<0.05). The difference between postoperative and preoperative Hb (△Hb1) was highest in group 1 (P<0.05). Conclusion For patients with valvular heart disease, intraoperative-only infusion of red blood cell suspension is associated with a better prognosis at discharge and during follow-up.

Hepatic encephalopathy is a difficult and critical disease with rapid progression and limited treatment methods in the field of liver disease， and it is urgently needed to make breakthroughs in its pathogenesis. Selection of appropriate prevention and treatment strategies is of great importance in delaying disease progression and reducing the incidence and mortality rates. This article reviews the theory of “turbid toxin pathogenesis” and related prevention and treatment strategies for hepatic encephalopathy in traditional Chinese medicine/Zhuang medicine， proposes a new theory of “turbid toxin pathogenesis”， analyzes the scientific connotations of “turbid”， “toxin”， and the theory of “turbid toxin pathogenesis”， and constructs the “four-step” prevention and treatment strategies for hepatic encephalopathy， thereby establishing the new clinical prevention and treatment regimen for hepatic encephalopathy represented by “four prescriptions and two techniques” and clarifying the effect mechanism and biological basis of core prescriptions and techniques in the prevention and treatment of hepatic encephalopathy， in order to provide a reference for the prevention and treatment of hepatic encephalopathy.

Objective To evaluate the caregiving capacity of family caregivers of children with hirschsprung's disease(HD)and to establish an evidence base for developing family-empowered home care interventions.Methods An explanatory sequential mixed-method design was employed.A total of 308 family caregivers of HD patients admitted to the Pediatric Surgery Department of a tertiary children's hospital in Shanghai,were enrolled from July 2019 to July 2024.Quantitative data were collected in 2024 using a demographic questionnaire,the Zarit Caregiver Burden Interview and the Family Caregiver Task Inventory.Semi-structured interviews were conducted in August to December 2024 with 15 caregivers of children with HD with poor caregiving skills.Quantitative data were analyzed using nonparametric tests and multiple linear regression,and qualitative information was refined through Colaizzi's 7-step analysis of themes.Results In the end,281 valid questionnaires were recovered.The total caregiving competence was 12.00(6.00,20.00).Multivariate analysis identified significant predictors of caregiving competence,including children in the postoperative dilation period,frequency of enterocolitis episodes,place of residence monthly household income and burden of caregiving were the factors influencing the ability of family caregivers to take care of their children(P＜0.05).Qualitative analysis revealed 4 themes:persistent multidimensional adaptive challenges,disparity in clinical caregiving skills,systemic support limitations,and role multiplicity strain.Conclusion Family caregivers of children with HD face multidimensional stressors,necessitating a dynamic,family-centered support ecosystem.Strategic interventions should focus on enhancing clinical caregiving literacy while improving access to socioeconomic resources and establishing role-specific support networks.

Objective:To explore the incidence, clinical features, genetic variant characteristics and prognosis of Glutaric acidemia type I (GA1) among neonates from Henan Province.Methods:A total of 814 625 neonates undergoing screening for inherited metabolic diseases by tandem mass spectrometry (MS/MS) at the Third Affiliated Hospital of Zhengzhou University from January 2016 to December 2022 were selected as the study subjects. A retrospective method was adopted to collect the clinical data of the patients. Whole exome sequencing was carried out to detect GCDH gene variants in individuals with positive results by GA1 newborn screening, and Sanger sequencing was used to verify the candidate variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the pathogenicity of candidate variants was rated. This study was approved by the Medical Ethics Committee of the Hospital (Approval Number: 2019 Medical Ethics Review No. 67). Results:Eight cases of GA1 were diagnosed among the 814 625 neonates. Blood glutaryl carnitine (C5DC) and urine glutaric acid (GA) levels of the 8 children were higher than the normal reference values. In total 12 variants were detected, all of which were missense variants. c. 1064G>A (p.Arg355His) was the most common one, accounting for 21.4% (3/14). Three GCDH gene variants, including 1297G>C (p.Ala433Pro), c. 467G>A (p.Gly156Asp) and c. 1125T>G (p.Cys375Trp), were previously unreported. REVEL software analysis predicted that all of the three variants were harmful. 3D protein structure modeling indicated that the three variants may cause amino acid residue alterations, and c. 1297G>C (p.Ala433Pro) and c. 1125T>G (p.Cys375Trp) may result in increase in hydrogen bonds and may affect the function of GCDH protein. By December 2023, one of the eight children had deceased, and another child had severe clinical symptoms with poor prognosis. Six children had a good prognosis, of which two had mild motor development delay and four had normal development without clinical symptoms. Conclusion:The incidence of GA1 in newborns screened by MS/MS in Henan Province is 1/101 828, and the carrier rate of pathogenic GCDH variants is 1/160. The c. 1064G>A (p.Arg355His) may be the hotspot variant of the GCDH gene among children with GA1 in Henan. Discovery of the three novel variants has enriched the mutational spectrum of the GCDH gene and provide a basis for the early diagnosis, treatment, prognosis and genetic counseling of this disease.

Objective To systematically retrieve,assess and synthesise regarding the respiratory training for patients with spinal cord injury and to provide a reference for clinical practice.Methods According to the 6S Evidence Pyramid Model,following databases were searched for literature in relation to the respiratory training for patients with spinal cord injury:UpToDate,BMJ Best Practice,Joanna Briggs Institute Database of Systematic Reviews and Implementations,National Institute for Health and Clinical Excellence(NICE),National Guideline Clearinghouse(NGC),Registered Nurses'Association of Ontario(RNAO),Medlive,American Spinal Injury Association(ASIA),Physiotherapy Evidence Database(PEDro),The Cochrane Library,PubMed,Web of Science,CNKI,Wanfang Data,SinoMed and the websites of the Journals published by Chinese Medical Association.The types of literature included clinical decisions,practice guidelines,expert consensus,evidence summaries,systematic reviews/Meta-analyses,and original researches.The search period was from the inception of databases to 30th December,2023.Two researchers independently evaluated the quality of the literature and summarised the evidences.Results Fifteen studies were ultimately included,comprising 3 clinical decisions,3 guidelines,6 systematic reviews and 3 randomised controlled trials.A total of 21 best pieces of evidence were summarised,covering 5 aspects:pre-training assessment,training content,training equipment,training intensity and frequency,and effectiveness evaluation.Conclusion The best evidence of respiratory training for patients with spinal cord injury summarised in this study can provide the evidence-based support for healthcare professionals to formulate standardised respiratory training strategies.

OBJECTIVE: To explore the incidence, clinical features, genetic variant characteristics and prognosis of Glutaric acidemia type I (GA1) among neonates from Henan Province. METHODS: A total of 814 625 neonates undergoing screening for inherited metabolic diseases by tandem mass spectrometry (MS/MS) at the Third Affiliated Hospital of Zhengzhou University from January 2016 to December 2022 were selected as the study subjects. A retrospective method was adopted to collect the clinical data of the patients. Whole exome sequencing was carried out to detect GCDH gene variants in individuals with positive results by GA1 newborn screening, and Sanger sequencing was used to verify the candidate variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the pathogenicity of candidate variants was rated. This study was approved by the Medical Ethics Committee of the Hospital (Ethics Number: 2019 Medical Ethics Review No. 67). RESULTS: Eight cases of GA1 were diagnosed among the 814 625 neonates. Blood glutaryl carnitine (C5DC) and urine glutaric acid (GA) levels of the 8 children were higher than the normal reference values. In total 12 variants were detected, all of which were missense variants. c.1064G>A (p.Arg355His) was the most common one, accounting for 21.4% (3/14). Three GCDH gene variants, including 1297G>C (p.Ala433Pro), c.467G>A (p.Gly156Asp) and c.1125T>G (p.Cys375Trp), were previously unreported. REVEL software analysis predicted that all of the three variants were harmful. 3D protein structure modeling indicated that the three variants may cause amino acid residue alterations, and c.1297G>C (p.Ala433Pro) and c.1125T>G (p.Cys375Trp) may result in increase in hydrogen bonds and affect the function of GCDH protein. By December 2023, one of the eight children had deceased, and another child had severe clinical symptoms with poor prognosis. Six children had a good prognosis, of which two had mild motor development delay and four had normal development without clinical symptoms. CONCLUSION The incidence of GA1 in newborns screened by MS/MS in Henan Province is 1/101 828, and the carrier rate of pathogenic GCDH variants is 1/160. The c.1064G>A (p.Arg355His) may be the hotspot variant of the GCDH gene among children with GA1 in Henan. Discovery of the three novel variants has enriched the mutational spectrum of the GCDH gene and provide a basis for the early diagnosis, treatment, prognosis and genetic counseling of this disease.
Humans ; Amino Acid Metabolism, Inborn Errors/epidemiology* ; Glutaryl-CoA Dehydrogenase/chemistry* ; Infant, Newborn ; Female ; Neonatal Screening/methods* ; Male ; Brain Diseases, Metabolic/epidemiology* ; China/epidemiology* ; Retrospective Studies ; Mutation ; Genetic Variation ; Glutarates

Objective To explore the current status of minor children parenting concerns among young and middle-aged breast cancer patients and investigate the influencing factors based on a random forest model so as to provide references for clinical interventions.Methods A convenience sampling method was used to select breast cancer patients undergoing treatment in our hospital between April and December 2023.A self-designed general information questionnaire,the Chinese version of parenting concerns questionnaire(PCQ),perceived social support scale(PSSS),concern about recurrence scale(CARS),and the brief illness perception questionnaire(BIPQ)were used for the study.A random forest model and the least absolute shrinkage and selection operator(LASSO)were employed to prioritise variables and filtered by significance.The selected variables were then incorporated into the multiple linear regression analysis.Results A total of 260 patients completed the study.The score of minor children parenting concerns of young and middle-aged breast cancer patients was 51.1±6.4.The multiple linear regression analysis,which included variables determined by random forest and LASSO regression(and sorted by the importance of influencing factors),showed that higher disease perception,lower perceived social support,greater concern about cancer recurrence,stage IV tumors,being divorced/widowed,and having more minor children were associated with higher parenting concerns among young and middle-aged breast cancer patients(all P<0.05),accounting for 57.0%of the total variance.Conclusion The minor children parenting concerns in young and middle-aged breast cancer patients are at a moderately high level and are influenced by a variety of factors.Healthcare professionals should develop targeted measures and interventions to reduce the parenting concerns among the patients.

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.