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.

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

OBJECTIVE: To describe survival trends and global patterns of esophageal cancer (EC) using survival data from population-based cancer registries. METHODS: We systematically searched PubMed, EMBASE, Web of Science, SEER, and SinoMed databases for articles published up to 31 December 2023. Eligible EC survival estimates were evaluated according to country or region, period, sex, age group, pathology, and disease stage. RESULTS: After 2010, Jordan exhibited the highest age-standardized 5-year relative survival rates (RSRs)/net survival rates (NSRs) at 41.1% between 2010 and 2014, while India had the lowest, at 4.1%. Survival rates generally improved with diagnostic age across most countries, with significant increases in South Korea and China, of 12.7% and 10.5% between 2000 and 2017, respectively. Survival was higher among women compared to men, ranging from 0.4%-10.9%. Survival rates for adenocarcinoma and squamous cell carcinoma were similar, differing by about 4%. In China, the highest age-standardized RSRs/NSRs was 33.4% between 2015 and 2017. Meanwhile, the lowest was 5.3%, in Qidong (Jiangsu province) between 1992-1996. CONCLUSION Global EC survival rates have improved significantly in recent decades, but substantial geographical, sex, and age disparities still exist. In Asia, squamous cell carcinoma demonstrated superior survival rates compared to adenocarcinoma, while the opposite trend was observed in Western countries. Future research should clarify the prognostic factors influencing EC survival and tailor prevention and screening strategies to the changing EC survival patterns.
Humans ; Esophageal Neoplasms/mortality* ; Registries ; Male ; Female ; Survival Analysis ; Middle Aged ; Survival Rate ; Aged ; Global Health

Objective To explore the risk factors for oral mucosal pressure injuries(OMPI)in patients with endotracheal intubation in the emergency intensive care unit(EICU)and to construct a nomogram prediction model based on these factors.Methods A case-control study design was adopt-ed to retrospectively collect clinical data from 209 adult patients with endotracheal intubation admitted to EICU.The patients were divided into OMPI group(53 patients)and non-OMPI group(156 pa-tients)based on whether OMPI occurred during the observation period.The clinical data of the two groups were analyzed,and multivariate Logistic regression analysis was used to screen risk factors for OMPI in patients with endotracheal intubation in the EICU.R software was used to draw a nomogram prediction model,and the predictive performance of the model was evaluated through the receiver oper-ating characteristic(ROC)curve,calibration curve,and decision curve analysis.Results Statistical-ly significant differences were observed between the two groups in prone position ventilation,vasocon-strictor use,consciousness at the time of intubation,Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score at the time of intubation,and duration of endotracheal intubation(P＜0.05).The results of multivariate Logistic regression analysis showed that prone position ventilation(OR=2.545,95％CI,1.261 to 5.135),vasoconstrictor use(OR=1.984,95％CI,1.162 to 3.387),inability to express complaints at time of intubation(OR=3.618,95％CI,1.891 to 6.924),high APACHE 11 score(OR=2.394,95％CI,1.322 to 4.336),and long duration of endotracheal in-tubation(OR=3.995,95％CI,1.857 to 8.593)were all risk factors for OMPI in patients with en-dotracheal intubation in the EICU(P＜0.05).ROC curve analysis showed that the area under the curve of the nomogram prediction model was 0.881;calibration curve analysis showed that the mean absolute error between the predicted probability and the actual probability of the model was 0.016;and decision curve analysis showed that the prediction model had practical value in clinical practice.Conclusion Prone position ventilation,vasoconstrictor use,inability to express complaints at the time of intubation,high APACHE Ⅱ score,and long duration of endotracheal intubation are all risk factors for OMPI in patients with endotracheal intubation in the EICU.The nomogram model con-structed based on these factors has good predictive performance for OMPI risk.

Objective:To investigate the protective effect of N-acetylcysteine (NAC) against α-amanitin (α-AMA)-induced liver injury via regulation of mitochondrial dynamic imbalance.Methods:Thirty-two ICR mice were randomly (random number) assigned to four groups ( n = 8 per group): normal control, NAC control, α-AMA poisoning, and α-AMA + NAC treatment group. After modeling, behavioral changes were observed and survival curves were plotted. Liver function markers and oxidative stress indicators were measured using ELISA. Pathological damage in liver tissue was examined, and mitochondrial ultrastructural changes were observed via transmission electron microscopy, followed by mitochondrial injury scoring. Survival rates were analyzed using the Kaplan–Meier method. One-way ANOVA was used for intergroup comparisons, followed by pairwise comparisons. Results:Compared with the control group, α-AMA intoxication significantly reduced survival rates and increased serum ALT and AST levels ( P < 0.05). Liver tissues exhibited disordered hepatic cord arrangement, cytoplasmic loosening, and edema. Mitochondria showed moderate to severe swelling, cristae fragmentation, matrix dissolution, and vacuolation, along with increased injury scores ( P < 0.05). Oxidative markers MDA and ROS were elevated, while antioxidant enzymes SOD and CAT were decreased (all P < 0.05). Mitochondrial activity was impaired, expression of fusion proteins OPA1, MFN1, and MFN2 was downregulated, and fission protein DRP1 was upregulated (all P < 0.05). Compared with the α-AMA group, NAC treatment significantly improved survival, reduced ALT and AST levels ( P < 0.05), alleviated pathological and mitochondrial ultrastructural damage, decreased MDA and ROS, increased SOD and CAT (all P < 0.05), enhanced mitochondrial activity, upregulated OPA1, MFN1, and MFN2, and downregulated DRP1 (all P < 0.05). No significant differences were observed between the normal and NAC control groups. Conclusions:NAC may attenuate α-AMA-induced acute liver injury by maintaining mitochondrial dynamic homeostasis.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

Objective To improve the efficiency of hospital antimicrobial management and ensure rational clinical use of antimicrobial agents with the aid of antimicrobial stewardship(AMS)empowered by digital intelligence tech-nology in hospital.Methods Information systems such as early warning of antimicrobial indexes,closed-loop ma-nagement of microbial detection information,and decision-making system of antimicrobial resistance monitoring data were applied to the traditional AMS system.Through hospital information systems(HIS)to collect data about thera-peutic antimicrobial use and healthcare-associated infection(HAI)quality control indexes of hospitalized patients in a tertiary first-class public hospital in Shenzhen City before and after digital technology improvement,indexes of 2021 and 2022 were as control group(before improvement)and observation group(after improvement)respective-ly,improvement trend of antimicrobial management was compared.Results After upgrading and renovating the hospital information system,hospital antimicrobial management indexes improved significantly compared to before the renovation.The use rate of antimicrobial agents and the preventive use rate of antimicrobial agents in class Ⅰincision surgery in patients in the observation group were both lower than those in the control group(27.0％vs 38.8％,20.9％vs 23.8％,respectively,both P＜0.05).Antimicrobial use density in hospitalized patients in the observa-tion group was lower than that in the control group([33.27±3.03]DDDs vs[42.06±4.42]DDDs),difference was statistically significant(t=13.11,P＜0.001).The observation group had a higher qualified rate for evaluating antimicrobial medical orders compared to the control group(98.5％vs 96.8％).The pathogenic detection rate of hospitalized patients before therapeutic antimicrobial use and pathogen detection rate related to HAI diagnosis were both higher than those in the control group(87.1％vs 84.5％,99.0％vs 95.4％,respectively),differences were both statistically significant(both P＜0.05).Conclusion Empowering the hospital's AMS system with digital technology can promote more scientific,standardized,efficient,and rational antimicrobial management in hospitals.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Objective:This article aims to explore the influencing factors of short-term poor prognosis in patients with coronary heart disease(CHD)and the predictive value of ambulatory arterial stiffness index(AASI)and heart rate variability triangular index(TI).Methods:A total of 300 CHD patients treated in Huai'an Second People's Hos-pital between January 2019 and January 2022 were selected.According to incidence of major adverse cardiovascular events(MACE)during 6-month follow-up,they were divided into good prognosis group(n=210)and poor prog-nosis group(n=90).Clinical data,AASI and TI were compared between two groups.Multivariate Logistic regres-sion was used to analyze influencing factors of poor prognosis during 6-month follow-up in CHD patients;receiv-er operating characteristic(ROC)curve was used to analyze the predictive value of AASI and TI for poor prognosis during 6-month follow-up in CHD patients.Results:Compared with patients in good prognosis group,those in poor prognosis group had significant higher age,body mass index(BMI),high sensitive C reactive protein(hsCRP),low density lipoprotein cholesterol(LDL-C),fibrinogen(Fg),AASI,TI,and proportions of smoking history,diabetes history and multi-vessel coronary disease(P＜0.01 all).Multivariate Logistic regression indicated that AASI(OR=5.318,95％CI 1.728～16.372,P=0.004),TI(OR=3.463,95％CI 1.155～10.384,P=0.027),age(OR=1.912,95％CI 1.046～1.990,P=0.033),smoking history(OR=1.791,95％CI 1.089～1.844,P=0.006),diabetes history(OR=1.645,95％CI 1.008～1.793,P＜0.001),BMI(OR=1.732,95％Cl 1.013～1.242,P=0.030),hsCRP(OR=3.731,95％CI 1.466～6.454,P=0.017),LDL-C(OR=3.732,95％CI 1.546～9.424,P=0.023),Fg(OR=4.504,95％CI 2.253～11.012,P=0.007)and multi-vessel coronary disease(OR=5.054,95％CI 2.876～13.445,P=0.029)were independent risk factors for poor prognosis during 6-month follow-up in CHD patients.Compared with single detection of AASI(AUC=0.828)and TI(AUC=0.844),their combined detection had significant higher predictive value(AUC=0.916)for poor prognosis during 6-month follow-up in CHD patients(Z=2.503,2.030,P＜0.05 both).Conclusion:Age,basic medical history,inflammatory factors,blood lipid and number of diseased vessels could all affect the prognostic outcome of CHD pa-tients.AASI combined with TI has good predictive value for short-term prognosis in CHD patients.