Objective To investigate the effects of laparoscopic sleeve gastrectomy(LSG)on the cardiac structure and function in obese patients with heart failure(HF)and compare the efficacy of LSG across obese patients with different HF types.Methods This study included 33 obese patients with HF who underwent LSG.The clinical indicators were compared between before operation and 12 months after operation.Repeated measures analysis of variance was employed to evaluate the changes in echocardiographic parameters before operation and 3,6,and 12 months after operation.Patients were allocated into a HF with preserved ejection fraction group(n=17),a HF with mildly reduced ejection fraction group(n=5)and a HF with reduced ejection fraction(HFrEF)group(n=11)based on left ventricular ejection fraction(LVEF)before operation for subgroup analyses of the effects of LSG on the cardiac structure and function of obese patients with HF.The paired samples t-test was conducted to assess the degree of cardiac structural and functional alterations after LSG.Results The 33 patients included 69.7% males,with an average age of(35.3±9.9)years,and a body mass index(BMI)of(51.2±9.8)kg/m².The median follow-up was 9.0(5.0,13.3)months.Compared with the preoperative values,the postoperative BMI(P=0.002),body surface area(BSA)(P=0.009),waist circumference(P=0.010),hip circumference(P=0.031),body fat content(P=0.007),and percentage of patients with cardiac function grades Ⅲ-IV(P<0.001)decreased.At the 12-month follow-up left atrial diameter(P=0.006),right atrial long-axis inner diameter(RAD1)(P<0.001),right atrial short-axis inner diameter(RAD2)(P<0.001),right ventricular inner diameter(P=0.002),interventricular septal thickness at end-diastolic(P=0.002),and left ventricular end-diastolic volumes(P=0.004)and left ventricular end-systolic volumes(P=0.003) all significantly reduced compared with preoperative values.Additionally,left ventricular fractional shortening and LVEF improved(both P<0.001).Subgroup analyses revealed that cardiac structural parameters significantly decreased in the HF with preserved ejection fraction,HF with mildly reduced ejection fraction,and HFrEF subgroups compared with preoperative values.Notably,the HFrEF group demonstrated the best performance in terms of left atrial diameter(P=0.003),left ventricular inner diameter at end-diastole(P=0.008),RAD1(P<0.001),RAD2(P=0.004),right ventricular inner diameter(P=0.019),left ventricular end-diastolic volume(P=0.004)and left ventricular end-systolic volume(P=0.001),cardiac output(P=0.006),tricuspid regurgitation velocity(P=0.002),and pulmonary artery systolic pressure(P=0.001) compared to preoperatively.Postoperative left ventricular fractional shortening(P<0.001,P=0.003,P<0.001)and LVEF(P<0.001,P=0.011,P=0.001)became higher in all the three subgroups than the preoperative values.Conclusions LSG decreased the body weight,BMI,and BSA,improved the cardiac function grade,reversed the enlargement of the left atrium and left ventricle,reduced the right atrium and right ventricle,and enhanced the left ventricular systolic function.It was effective across obese patients with different HF types.Particularly,LSG demonstrates the best performance in improving the structures of both atria and ventricles in obese patients with HFrEF.
Humans ; Male ; Female ; Gastrectomy/methods* ; Heart Failure/complications* ; Adult ; Obesity/physiopathology* ; Laparoscopy ; Middle Aged ; Heart/physiopathology* ; Stroke Volume

Objective To investigate whether successful percutaneous coronary intervention(PCI)could improve symptoms and quality of life(QOL)in left main disease and/or 3-vessel disease patients with diabetes.Methods Patients with left main disease and/or 3-vessel disease who underwent PCI in the First Affiliated Hospital of Air Force Medical University from April 2018 to May 2021 were consecutively enrolled and subdivided into 2 groups:diabetes and no diabetes.Detailed baseline characteristics,symptoms,including dyspnea and angina,assessed with the Rose dyspnea scale(RDS),Seattle angina questionnaire(SAQ),the European quality of life-5 dimensions(EQ-5D)and 12-item short-form health survey(SF-12)questionnaire respectively,procedural details,and 1 month and 1 year follow-up data were collected.Results Among 440 left main disease and/or 3-vessel disease patients,disease was present in 176(40.00％),who had more hypertension,peripheral artery disease,and LCX lesion(all P＜0.05).The incidence of major adverse cardiovascular events(MACE)and all-cause mortality were similar between the two groups(both P＞0.05)at 1 month follow-up,while all-cause mortality in diabetes patients was significantly higher than those without diabetes at 1 year follow-up(P=0.013).Low left ventricular ejection fraction was an independent risk factor for MACE and all-cause mortality at 1 month and 1 year follow-up after successful revascularization(all P＜0.05).Most importantly,symptoms,including dyspnea and angina,and QOL were markedly improved regardless of diabetes both at 1 month and 1 year follow-up(all P＜0.05).Diabetes patients showed improved dyspnea and QOL at similar degree to the non-diabetes patients(all P＞0.05)and a more significantly relieved angina(P=0.013).Additionally,the number of chronic total occlusion(CTO)per patient was identified as an independent risk factor of dyspnea(OR 0.723,95％CI 0.525～0.997,P=0.048)and angina relief(OR 0.686,95％CI 0.473～0.995,P=0.047),and the contrast volume(OR 0.995,95％CI 0.992～0.999,P=0.008)as an independent risk factor of QOL improvement in diabetic patients.Conclusions Successful PCI is beneficial for relieving symptoms and improving quality of life in patients with diabetes who have left main disease and/or 3-vessel disease.

Objective:To summarize the current situation and shortcomings of China's health technology innovation policies,and provide reference for policy formulation and improvement.Methods:Text mining was used to sort out 24 policy documents related to health technology innovation issued by the national and provincial levels since the 13th Five Year Plan period.A PMC index evaluation model for health technology innovation policies was established,and a quantitative analysis of health technology innovation policies was conducted through 9 primary indicators and 43 secondary indicators.Results:Among the 24 policies,2 were rated as perfect,8 were rated as excellent,and 14 were rated as acceptable.Conclusions and Suggestions:China's policies on health and medical science and technology innovation have been basically improved.They can be further refined by focusing on core and key technologies,emphasizing clinical research and transformation,and advancing digital and intelligent strategies.

Many female patients with inflammatory bowel disease (IBD) are of reproductive age, and addressing fertility-related issues is an important part of comprehensive treatment. Although the fertility of female IBD patients does not differ significantly from that of the general population, many women with IBD opt for childlessness. This voluntary childlessness may stem from concerns about the adverse effects of IBD and its treatment on fertility, pregnancy, and newborns. A lack of in depth understanding of fertility, socioeconomic status, demographic factors, and insufficient professional medical advice may also contribute to patients' decision to choose voluntary childlessness. This article analyzes the factors influencing voluntary childlessness in female IBD patients and summarizes recommendations aimed at reducing trend, to enhance fertility awareness among female IBD patients and providing useful references for developing reasonable fertility plans.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate whether successful percutaneous coronary intervention(PCI)could improve symptoms and quality of life(QOL)in left main disease and/or 3-vessel disease patients with diabetes.Methods Patients with left main disease and/or 3-vessel disease who underwent PCI in the First Affiliated Hospital of Air Force Medical University from April 2018 to May 2021 were consecutively enrolled and subdivided into 2 groups:diabetes and no diabetes.Detailed baseline characteristics,symptoms,including dyspnea and angina,assessed with the Rose dyspnea scale(RDS),Seattle angina questionnaire(SAQ),the European quality of life-5 dimensions(EQ-5D)and 12-item short-form health survey(SF-12)questionnaire respectively,procedural details,and 1 month and 1 year follow-up data were collected.Results Among 440 left main disease and/or 3-vessel disease patients,disease was present in 176(40.00％),who had more hypertension,peripheral artery disease,and LCX lesion(all P＜0.05).The incidence of major adverse cardiovascular events(MACE)and all-cause mortality were similar between the two groups(both P＞0.05)at 1 month follow-up,while all-cause mortality in diabetes patients was significantly higher than those without diabetes at 1 year follow-up(P=0.013).Low left ventricular ejection fraction was an independent risk factor for MACE and all-cause mortality at 1 month and 1 year follow-up after successful revascularization(all P＜0.05).Most importantly,symptoms,including dyspnea and angina,and QOL were markedly improved regardless of diabetes both at 1 month and 1 year follow-up(all P＜0.05).Diabetes patients showed improved dyspnea and QOL at similar degree to the non-diabetes patients(all P＞0.05)and a more significantly relieved angina(P=0.013).Additionally,the number of chronic total occlusion(CTO)per patient was identified as an independent risk factor of dyspnea(OR 0.723,95％CI 0.525～0.997,P=0.048)and angina relief(OR 0.686,95％CI 0.473～0.995,P=0.047),and the contrast volume(OR 0.995,95％CI 0.992～0.999,P=0.008)as an independent risk factor of QOL improvement in diabetic patients.Conclusions Successful PCI is beneficial for relieving symptoms and improving quality of life in patients with diabetes who have left main disease and/or 3-vessel disease.

Objective:To summarize the current situation and shortcomings of China's health technology innovation policies,and provide reference for policy formulation and improvement.Methods:Text mining was used to sort out 24 policy documents related to health technology innovation issued by the national and provincial levels since the 13th Five Year Plan period.A PMC index evaluation model for health technology innovation policies was established,and a quantitative analysis of health technology innovation policies was conducted through 9 primary indicators and 43 secondary indicators.Results:Among the 24 policies,2 were rated as perfect,8 were rated as excellent,and 14 were rated as acceptable.Conclusions and Suggestions:China's policies on health and medical science and technology innovation have been basically improved.They can be further refined by focusing on core and key technologies,emphasizing clinical research and transformation,and advancing digital and intelligent strategies.

Many female patients with inflammatory bowel disease (IBD) are of reproductive age, and addressing fertility-related issues is an important part of comprehensive treatment. Although the fertility of female IBD patients does not differ significantly from that of the general population, many women with IBD opt for childlessness. This voluntary childlessness may stem from concerns about the adverse effects of IBD and its treatment on fertility, pregnancy, and newborns. A lack of in depth understanding of fertility, socioeconomic status, demographic factors, and insufficient professional medical advice may also contribute to patients' decision to choose voluntary childlessness. This article analyzes the factors influencing voluntary childlessness in female IBD patients and summarizes recommendations aimed at reducing trend, to enhance fertility awareness among female IBD patients and providing useful references for developing reasonable fertility plans.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

An integrated evaluation model based on the combination of traditional trait identification and modern chemical analysis was used for the identification of key indexes of grade classification and the establishment of grade quality standard of Lycium barbarum fruits (LBF) from Ningxia genuine producing area. Under the guidance of herbal examination and market survey, particle size, skin color and imperfection were taken as the external trait indicators; focusing on the comprehensiveness and validity of the quality evaluation, 2-O-β-D-glucopyranosyl-L-ascorbic acid (AA-2βG), zeaxanthin dipalmitate (ZD) and volatile ether extractives were used as important parameters involving in quality evaluation in addition to the pharmacopoeia test items and determination indicators such as moisture, ash, water-soluble extractives, Lycium barbarum polysaccharide (LBP) and betaine. The quality differences between different traits and grades of LBF as well as the relevance between character and quality were further investigated by mathematical and statistical means such as t-test analysis, correlation analysis and principal component analysis (PCA). Furthermore, the activities of extractives from different grades of LBF were compared at cellular level. The results showed that LBF were mainly preferred for large and red ones; except for betaine, the other index components were relatively high (moisture and ash were low) in the first-grade and samples with bright-red skin color. Besides, there was a significant correlation between skin color and moisture, water-soluble extractives, LBP, AA-2βG and ZD contents. Considering the results of the study and the market situation, the present study took the grain size and skin color of LBF as the core of evaluation, and at the same time introduced the multi-indicator components including LBP, AA-2βG, ZD, water-soluble extractives and moisture to further guide the grading, and established the quality standard of LBF grades combining the trait indexes and the chemical composition. More importantly, in vitro cell models both preliminarily confirmed that the first-grade LBF played a more significant "nourishing liver and kidney" effect, which provided an experimental basis for the quality control of LBF and promoted the realization of high quality and good price.