Objective:To develop and validate a diagnostic model for pancreatic benign and malignant tumors using radiomics technology.Methods:A retrospective analysis was conducted on the clinical data of 113 patients with pancreatic tumors who underwent surgical treatment at the Hepatobiliary and Pancreatic Surgery Departments of the Second Hospital and the First Hospital of Hebei Medical University from January 2020 to December 2022. There were 59 male and 54 female patients, aged (55.3±16.8) years. Preoperative enhanced thin-slice computed tomography (CT) data and postoperative pathological diagnosis results were collected. Data from 74 patients at the Second Hospital were selected, and according to the random classification principle of 7∶3, the data of 52 patients were determined as the training set for model construction, while the remaining 22 patients' data served as the internal validation set. Data from 39 patients at the First Hospital of Hebei Medical University were used as the external validation set to assess the generali-zability of the established model. The region of interest in the lesions on CT images was analyzed using three-dimensional radiomics feature extraction, and the top 5 features were selected using feature selection methods. Radiomics models were established for the selected features using 17 classifiers. The performance of the models was evaluated using the area under curve (AUC) of the receiver operating characteristic (ROC).Results:Two hundred and fifty-five models were established with 15 feature selection methods and 17 classifiers. 7 models with the AUC greater than 0.7 were selected, among which the best one was LASSO-K neighbors classifier model, constructed using the LASSO feature selection method and the k-nearest neighbors algorithm, achieving AUC values of 0.933 (95% CI: 0.859-0.984) in the training set, 0.973 (95% CI: 0.896-1.000) in the validation set, and 0.774 (95% CI: 0.624-0.908) in the external validation set, with satisfactoryclassification and generalization ability. Conclusion:The radiomics-based diagnostic model for pancreatic benign and malignant tumors can effectively distinguish the benignancy and malignancy of tumors. The LASSO-K neighbors classifier model demonstrated high accuracy and reliability in this study.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

Objectives:To explore the value of non-invasive myocardial work combined with myocardial contrast echocardiography(MCE)in the early diagnosis of coronary artery disease and its efficacy in stratifying the severity of coronary vessel lesions.Methods:A total of 130 patients with suspected coronary artery disease admitted to the First Affiliated Hospital of Dalian Medical University from June 2024 to January 2025 were enrolled in this study.All patients underwent echocardiography and MCE after admission,and coronary angiography(CAG).Based on CAG results,patients were divided into non-CAD group(n=45,coronary artery stenosis<50%)and CAD group(n=85,coronary artery stenosis≥50%).Patients in CAD group were further divided into low-score CAD group(≤49 points,n=43)and high-score CAD group(>49 points,n=42)according to the median of Gensini score(49 points).Non-invasive MW indices and quantitative MCE parameters were assessed.A binary logistic regression model was used to construct a combined diagnostic model,and the value of each parameter in diagnosing CAD and evaluating the severity of coronary lesions was calculated.The receiver operating characteristic(ROC)curve of subjects was estimated,and the area under the curve(AUC)was calculated to evaluate its sensitivity and specificity for early diagnosis of coronary heart disease.Results:Compared with the non-CAD group,the global longitudinal strain,global work index(GWI),and global constructive work(GCW)in both low-score and high-score CAD groups were significantly lower(all P<0.05),and the global work efficiency in the high-score CAD group was significantly reduced(P<0.05).MCE indices in both low-score and high-score CAD groups were significantly lower than those in the non-CAD group(all P<0.05).The multivariate logistic stepwise regression analysis and ROC curve showed that GWI(OR=0.997,95%CI:0.995-0.999,P=0.003)and A value(representing the peak intensity of the curve,reflecting myocardial blood volume(OR=0.415,95%CI:0.246-0.698,P=0.001)were independent predictors of low-score CAD.The combined diagnostic sensitivity and specificity for low-score coronary artery disease were 72.1%and 88.9%respectively,with an AUC of 0.851.GCW(OR=0.997,95%CI:0.995-1.000,P=0.019)and β-value(OR=0.000,95%CI:0.000-0.003,P<0.001)were independent predictors of high-score CAD.The combined diagnostic sensitivity and specificity for high-score coronary artery disease were 88.1%and 88.9%respectively,with an AUC of 0.934.Conclusions:Both non-invasive myocardial work parameters and MCE parameters have high diagnostic efficacy for coronary artery lesions of various degrees.The combined application of the two methods significantly improves the accuracy of coronary artery disease diagnosis,with improved sensitivity and specificity than single technique.Our results provide a new non-invasive comprehensive diagnostic model for clinical early diagnosis and risk stratification of coronary artery disease.

Objective:To systematically evaluate the effect of the Dietary Approaches to Stop Hypertension (DASH) diet on common metabolic diseases.Methods:Following the PICOS framework to construct the search formula, we systematically searched the Web of science, PubMed, Cochrane Library, CNKI, Wanfang, and VIP databases for randomized controlled trials (RCTs) investigating the effect of the DASH diet on multiple metabolic indices including body weight, body mass index (BMI), fasting blood glucose, triglycerides, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in patients with metabolic disorders (e.g., obesity, diabetes mellitus, and hypertension) published both in China and abroad from 1 January 2000 to 31 December 2023. The search terms were combinations of MeSH and free-text terms, and the raw data obtained from the search were screened according to the inclusion and exclusion criteria to determine the articles that were finally included in the analysis. The differences in these metabolic indices were compared between the intervention group group (using DASH diet) and the control group. The quality of the studies was assessed quantitatively using the modified Jadad scale and qualitatively using the Risk of Bias 2 (RoB 2) tool by two reviewers independently. Data analysis was conducted using Stata 18.0 software, with the I 2 test for heterogeneity evaluation and funnel plots for qualitative assessment of publication bias. Results:A total of 13 RCTs were included, involving 1 653 participants. Meta-analysis showed that the intervention group had more favorable effects on SBP ( SMD:-0.91, 95%CI: -1.55–-0.27; Z=-2.79, P<0.005) and DBP ( SMD: -0.98, 95%CI: -1.62–-0.33; Z=-2.96, P<0.05) than the control diet. Fasting blood glucose ( SMD: 0.21, 95% CI: 0.01–0.41; Z=2.06, P=0.04) was statistically significant. There were no significant differences in body weight, BMI, fasting blood glucose, and triglyceride outcomes betweeen these two groups. Conclusions:DASH diet is effective in controlling SBP and DBP in patients with metabolic disorders and may be useful in clinical adjunctive therapy. However, current evidence does not support an independent improvement in glycolipid metabolic markers.

An increasing number of studies have shown that there is a close relationship between Alzheimer's disease(AD)and diabetes mellitus(DM).Traditional Chinese medicine holds that"kidney yin deficiency"is the common pathogenesis of these two diseases,while modern medicine believes that their pathogenesis involves abnormal aggregation of amyloid proteins,insulin deficiency and resistance,inflammatory response,oxidative stress,and autophagy,among others.The well-known traditional Chinese medicine formula Liuwei Dihuang Pills plays a significant role in the treatment of these two diseases with the same therapeutic approach.Therefore,this article will explore the connection between Liuwei Dihuang Pills and the treatment of AD and DM from different aspects;analyze the common etiology and pathogenesis of AD and DM,and explain the mechanism of prevention and treatment of Liuwei Dihuang Pills,with the aim of providing new ideas and methods for the integrated traditional and Western medicine prevention and treatment of AD and DM in the future.

Objective:To systematically evaluate the effect of the Dietary Approaches to Stop Hypertension (DASH) diet on common metabolic diseases.Methods:Following the PICOS framework to construct the search formula, we systematically searched the Web of science, PubMed, Cochrane Library, CNKI, Wanfang, and VIP databases for randomized controlled trials (RCTs) investigating the effect of the DASH diet on multiple metabolic indices including body weight, body mass index (BMI), fasting blood glucose, triglycerides, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in patients with metabolic disorders (e.g., obesity, diabetes mellitus, and hypertension) published both in China and abroad from 1 January 2000 to 31 December 2023. The search terms were combinations of MeSH and free-text terms, and the raw data obtained from the search were screened according to the inclusion and exclusion criteria to determine the articles that were finally included in the analysis. The differences in these metabolic indices were compared between the intervention group group (using DASH diet) and the control group. The quality of the studies was assessed quantitatively using the modified Jadad scale and qualitatively using the Risk of Bias 2 (RoB 2) tool by two reviewers independently. Data analysis was conducted using Stata 18.0 software, with the I 2 test for heterogeneity evaluation and funnel plots for qualitative assessment of publication bias. Results:A total of 13 RCTs were included, involving 1 653 participants. Meta-analysis showed that the intervention group had more favorable effects on SBP ( SMD:-0.91, 95%CI: -1.55–-0.27; Z=-2.79, P<0.005) and DBP ( SMD: -0.98, 95%CI: -1.62–-0.33; Z=-2.96, P<0.05) than the control diet. Fasting blood glucose ( SMD: 0.21, 95% CI: 0.01–0.41; Z=2.06, P=0.04) was statistically significant. There were no significant differences in body weight, BMI, fasting blood glucose, and triglyceride outcomes betweeen these two groups. Conclusions:DASH diet is effective in controlling SBP and DBP in patients with metabolic disorders and may be useful in clinical adjunctive therapy. However, current evidence does not support an independent improvement in glycolipid metabolic markers.

An increasing number of studies have shown that there is a close relationship between Alzheimer's disease(AD)and diabetes mellitus(DM).Traditional Chinese medicine holds that"kidney yin deficiency"is the common pathogenesis of these two diseases,while modern medicine believes that their pathogenesis involves abnormal aggregation of amyloid proteins,insulin deficiency and resistance,inflammatory response,oxidative stress,and autophagy,among others.The well-known traditional Chinese medicine formula Liuwei Dihuang Pills plays a significant role in the treatment of these two diseases with the same therapeutic approach.Therefore,this article will explore the connection between Liuwei Dihuang Pills and the treatment of AD and DM from different aspects;analyze the common etiology and pathogenesis of AD and DM,and explain the mechanism of prevention and treatment of Liuwei Dihuang Pills,with the aim of providing new ideas and methods for the integrated traditional and Western medicine prevention and treatment of AD and DM in the future.

Objective:To explore the lived experiences of family members of terminal ICU patients regarding their humanistic care needs and provide theoretical foundations for developing nursing care plans tailored to their needs.Methods:This study was a descriptive qualitative study. From April to December 2023, 16 family members of terminally ill ICU patients in Shanghai East Hospital, Tongji University were selected for semi-structured interviews using purposive sampling method, and the interview data were qualitatively analyzed using Colaizzi 7-step analysis.Results:The humanistic care needs of family members of terminally ill ICU patients can be categorized into five themes, namely, the need to know the condition at the first time; the need to participate in treatment and decision-making; the need to respect the wishes of terminally ill patients; the need for psychological care; and the need for social support.Conclusions:The humanistic care needs of family members of terminal ICU patients remain largely unmet. Nursing professionals should consider these needs and preferences and provide family members with professional guidance to help them establish positive coping mechanisms.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

Objectives:To explore the value of non-invasive myocardial work combined with myocardial contrast echocardiography(MCE)in the early diagnosis of coronary artery disease and its efficacy in stratifying the severity of coronary vessel lesions.Methods:A total of 130 patients with suspected coronary artery disease admitted to the First Affiliated Hospital of Dalian Medical University from June 2024 to January 2025 were enrolled in this study.All patients underwent echocardiography and MCE after admission,and coronary angiography(CAG).Based on CAG results,patients were divided into non-CAD group(n=45,coronary artery stenosis<50%)and CAD group(n=85,coronary artery stenosis≥50%).Patients in CAD group were further divided into low-score CAD group(≤49 points,n=43)and high-score CAD group(>49 points,n=42)according to the median of Gensini score(49 points).Non-invasive MW indices and quantitative MCE parameters were assessed.A binary logistic regression model was used to construct a combined diagnostic model,and the value of each parameter in diagnosing CAD and evaluating the severity of coronary lesions was calculated.The receiver operating characteristic(ROC)curve of subjects was estimated,and the area under the curve(AUC)was calculated to evaluate its sensitivity and specificity for early diagnosis of coronary heart disease.Results:Compared with the non-CAD group,the global longitudinal strain,global work index(GWI),and global constructive work(GCW)in both low-score and high-score CAD groups were significantly lower(all P<0.05),and the global work efficiency in the high-score CAD group was significantly reduced(P<0.05).MCE indices in both low-score and high-score CAD groups were significantly lower than those in the non-CAD group(all P<0.05).The multivariate logistic stepwise regression analysis and ROC curve showed that GWI(OR=0.997,95%CI:0.995-0.999,P=0.003)and A value(representing the peak intensity of the curve,reflecting myocardial blood volume(OR=0.415,95%CI:0.246-0.698,P=0.001)were independent predictors of low-score CAD.The combined diagnostic sensitivity and specificity for low-score coronary artery disease were 72.1%and 88.9%respectively,with an AUC of 0.851.GCW(OR=0.997,95%CI:0.995-1.000,P=0.019)and β-value(OR=0.000,95%CI:0.000-0.003,P<0.001)were independent predictors of high-score CAD.The combined diagnostic sensitivity and specificity for high-score coronary artery disease were 88.1%and 88.9%respectively,with an AUC of 0.934.Conclusions:Both non-invasive myocardial work parameters and MCE parameters have high diagnostic efficacy for coronary artery lesions of various degrees.The combined application of the two methods significantly improves the accuracy of coronary artery disease diagnosis,with improved sensitivity and specificity than single technique.Our results provide a new non-invasive comprehensive diagnostic model for clinical early diagnosis and risk stratification of coronary artery disease.