BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Objective:To explore the application value of deep learning reconstruction(DLR)of magnetic resonance(MR)on the image quality and lesion diagnostic efficacy of three-dimensional liver acceleration volume acquisition(3D-LAVA)sequences.Methods:A retrospective analysis was conducted on the image data of magnetic resonance imaging(MRI)of 45 patients who underwent MR contrast-enhanced examinations on abdomen at West China Hospital of Sichuan University from June 2023 to September 2023.For the venous phase of the LAVA sequence,conventional reconstruction and DLR were performed respectively to obtain conventional LAVA images and DLR-LAVA images at the venous phase.Two radiologists conducted subjective evaluation for the overall image quality,image clarity,artifacts,and diagnostic confidence of the two groups of images by using a 5-point scale.And then,the signal-to-noise ratio(SNR)of the images of liver,spleen,and lesion,as well as the contrast-to-noise ratio(CNR)of the liver,spleen to fat,muscle,and lesions were objectively evaluated.The two radiologists measured respectively the maximum and minimum diameters of the largest layer of the largest lesion,and compared the diagnostic efficacy of conventional LAVA and DLR-LAVA for lesions.The diagnostic efficacies of conventional LAVA and DLR-LAVA were respectively assessed by using the receiver operating characteristic(ROC)curve.Results:The consistency of the two radiologists'subjective evaluations for the image quality of the conventional LAVA and DLR-LAVA sequences was moderate and above moderate,with Kappa values ranging from 0.553 to 0.902,and P<0.001.Both radiologists found that the subjective image quality,image clarity,artifacts,and diagnostic confidence of the DLR-LAVA sequence were higher than those of the conventional LAVA sequence(Z Physician 1=4.24,5.49,3.50,4.47,Z Physician 2=4.15,3.12,3.77,4.26,P<0.05).The SNR values of the liver,spleen,and lesions in the DLR-LAVA images were all higher than those in the conventional LAVA sequence(t=-20.45,-18.58,-5.51,P<0.001).The CNR values between liver and fat,between liver and muscle,between liver and spleen,between spleen and fat,between spleen and muscle,and between lesion and liver in DLR-LAVA sequence were higher than those in the conventional LAVA sequence(t=-20.49,-19.94,-18.05,-16.74,P<0.001).There were no statistically significant differences in the measured values of the maximum and minimum diameters of the largest lesions between the conventional LAVA and DLR-LAVA sequences(P>0.05).The area under curve(AUC)values of physician 1 who used respectively the conventional LAVA and DLR-LAVA sequences were 0.897 and 0.916 in diagnosing lesions,while those of physician 2 were respectively 0.851 and 0.890.Conclusion:DLR can improve the image quality of the LAVA sequence for liver,and has a higher diagnostic value for lesions.

Objective:To explore the application value of deep learning reconstruction(DLR)of magnetic resonance(MR)on the image quality and lesion diagnostic efficacy of three-dimensional liver acceleration volume acquisition(3D-LAVA)sequences.Methods:A retrospective analysis was conducted on the image data of magnetic resonance imaging(MRI)of 45 patients who underwent MR contrast-enhanced examinations on abdomen at West China Hospital of Sichuan University from June 2023 to September 2023.For the venous phase of the LAVA sequence,conventional reconstruction and DLR were performed respectively to obtain conventional LAVA images and DLR-LAVA images at the venous phase.Two radiologists conducted subjective evaluation for the overall image quality,image clarity,artifacts,and diagnostic confidence of the two groups of images by using a 5-point scale.And then,the signal-to-noise ratio(SNR)of the images of liver,spleen,and lesion,as well as the contrast-to-noise ratio(CNR)of the liver,spleen to fat,muscle,and lesions were objectively evaluated.The two radiologists measured respectively the maximum and minimum diameters of the largest layer of the largest lesion,and compared the diagnostic efficacy of conventional LAVA and DLR-LAVA for lesions.The diagnostic efficacies of conventional LAVA and DLR-LAVA were respectively assessed by using the receiver operating characteristic(ROC)curve.Results:The consistency of the two radiologists'subjective evaluations for the image quality of the conventional LAVA and DLR-LAVA sequences was moderate and above moderate,with Kappa values ranging from 0.553 to 0.902,and P<0.001.Both radiologists found that the subjective image quality,image clarity,artifacts,and diagnostic confidence of the DLR-LAVA sequence were higher than those of the conventional LAVA sequence(Z Physician 1=4.24,5.49,3.50,4.47,Z Physician 2=4.15,3.12,3.77,4.26,P<0.05).The SNR values of the liver,spleen,and lesions in the DLR-LAVA images were all higher than those in the conventional LAVA sequence(t=-20.45,-18.58,-5.51,P<0.001).The CNR values between liver and fat,between liver and muscle,between liver and spleen,between spleen and fat,between spleen and muscle,and between lesion and liver in DLR-LAVA sequence were higher than those in the conventional LAVA sequence(t=-20.49,-19.94,-18.05,-16.74,P<0.001).There were no statistically significant differences in the measured values of the maximum and minimum diameters of the largest lesions between the conventional LAVA and DLR-LAVA sequences(P>0.05).The area under curve(AUC)values of physician 1 who used respectively the conventional LAVA and DLR-LAVA sequences were 0.897 and 0.916 in diagnosing lesions,while those of physician 2 were respectively 0.851 and 0.890.Conclusion:DLR can improve the image quality of the LAVA sequence for liver,and has a higher diagnostic value for lesions.

Objective:To investigate the consistency of myocardial extracellular volume between systole and diastole using dual-layer detector spectral CT.Methods:This was a cross-sectional study. Thirty-five patients who underwent cardiac spectral CT examination in West China Hospital of Sichuan University from April 2022 to December 2022 were retrospectively collected. Hematocrit was collected within 3 days before the CT scan. The delayed phases holographic spectral images in systole (45%) and diastole (75%) were obtained using dual-layer spectral CT. CT data were processed using a spectral post-processing workstation, and the extracellular volume (ECV) based on iodine density images, referred as CT-ECV, in systolic and diastolic phases were calculated, respectively. According to the American Heart Association′s 16-segment model of left ventricular, the standard short-axis images were constructed, and the myocardium was standardized into 16 segments at the basal, mid-cavity, and apical levels of the left ventricle. Two radiologists performed a subjective evaluation in the image quality of the CT-ECV images of the whole heart and the three sections in systole and diastole using a "five-point" scale. The ECV of the 16 segments and the whole heart in systole and diastole was calculated. The consistency of subjective evaluations between systole and diastole was assessed using Kappa statistics. Wilcoxon signed-rank tests were used to compare the differences in scores between systole and diastole. Paired sample t-test was used to compare the differences in CT-ECV scores between systole and diastole. The intraclass correlation coefficient was used to test the intra-and inter-observer consistency of CT-ECV measurements between two radiologists. P<0.05 was statistically significant. Results:There was good agreement between the two radiologists on subjective scores of CT-ECV image quality between systole and diastole ( Kappa>0.80), and there was no statistical difference in image quality among the basal, mid-cavity, and apical levels of the left ventricle and whole heart between systole and diastole ( P>0.05). The systolic and diastolic CT-ECV for the entire heart obtained through the delay phase were (33.29±3.46)% and (33.50±3.39)%, respectively, with no statistically significant difference ( t=-0.78, P=0.442). CT-ECV in systole and diastole were (34.15±3.94)% and (35.30±3.99)% for segment 8, (34.03±3.76)% and (35.46±3.74)% for segment 9, and (33.98±3.32)% and (35.05±3.98)% for segment 14, respectively. The mean values of the systolic CT-ECV of segments 8, 9 and 14 were significantly lower than those of diastolic CT-ECV ( t=-2.65, -3.26, -2.42, P=0.012, 0.003, 0.022, respectively). The ICCs for CT-ECV measurements of 16 segments by the two radiologists were greater than 0.90 in both systolic and diastolic, indicating good agreement. Conclusions:There is no significant difference in whole heart CT-ECV values between systolic and diastolic myocardial ECV based on dual-layer spectral CT. However, minor differences (less than 2%) are found between systolic and diastolic myocardial CT-ECV for some segments. Myocardial CT-ECV measurement should be performed on the same segment during the same phase to obtain stable and accurate ECV values.

OBJECTIVE: To develop and validate a user-friendly risk score for older mitral regurgitation (MR) patients, referred to as the Elder-MR score. METHODS: The China Senile Valvular Heart Disease (China-DVD) Cohort Study functioned as the development cohort, while the China Valvular Heart Disease (China-VHD) Study was employed for external validation. We included patients aged 60 years and above receiving medical treatment for moderate or severe MR (2274 patients in the development cohort and 1929 patients in the validation cohort). Candidate predictors were chosen using Cox's proportional hazards model and stepwise selection with Akaike's information criterion. RESULTS: Eight predictors were identified: age ≥ 75 years, body mass index < 20 kg/m², NYHA class III/IV, secondary MR, anemia, estimated glomerular filtration rate < 60 mL/min per 1.73 m², albumin < 35 g/L, and left ventricular ejection fraction < 60%. The model displayed satisfactory performance in predicting one-year mortality in both the development cohort (C-statistic = 0.73, 95% CI: 0.69-0.77, Brier score = 0.06) and the validation cohort (C-statistic = 0.73, 95% CI: 0.68-0.78, Brier score = 0.06). The Elder-MR score ranges from 0 to 15 points. At a one-year follow-up, each point increase in the Elder-MR score represents a 1.27-fold risk of death (HR = 1.27, 95% CI: 1.21-1.34, P < 0.001) in the development cohort and a 1.24-fold risk of death (HR = 1.24, 95% CI: 1.17-1.30, P < 0.001) in the validation cohort. Compared to EuroSCORE II, the Elder-MR score demonstrated superior predictive accuracy for one-year mortality in the validation cohort (C-statistic = 0.71 vs. 0.70, net reclassification improvement = 0.320, P < 0.01; integrated discrimination improvement = 0.029, P < 0.01). CONCLUSIONS The Elder-MR score may serve as an effective risk stratification tool to assist clinical decision-making in older MR patients.

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
Area Under Curve ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; China ; Deep Learning ; Humans ; ROC Curve ; Sensitivity and Specificity

Three compounds, including scolosprine C(1), uracil(2) and hypoxanthine(3), were isolated and purified from the ethyl acetate fraction of centipede by silica gel normal-phase column chromatography, reversed-phase medium pressure preparation chromatography, and high-pressure semi-preparative HPLC. The structure was elucidated through a combination of spectroscopic analyses [such as nuclear magnetic resonance(NMR) and mass spectrometry(MS)] and literature review. Among them, compound 1 was a new quinoline alkaloid. In previous reports, we have described the isolation and structure elucidation of one new and two known quinoline alkaloids. In this paper, we would report the isolation and structure elucidation of scolosprine C in detail.
Alkaloids ; Animals ; Arthropods ; Chilopoda ; Quinolines

Galangal (Alpinia officinaruim Hance) is the rhizome of the perennial herb belonging to Zingiberaceae family. There are many active components in galangal, such as volatile oil, flavonoids, terpenoids, phenylpropanoids and glycosides, among which the content of volatile oil is higher. The bioactivities of galangal volatile oil on health effect includesanti-inflammatory, anti-hypertension, anti-oxidation and prevention of cardiovascular diseases. Cardiovascular disease (CVD) is a kind of diseases related to circulatory system, which is also called circulatory system diseases. Over the past decade, the number of people dying from CVD has increased by 12.5％ worldwide, and it is now the leading cause of human death worldwide. Studies have shown that galangal volatile oil has good pharmacological effects in treating CVD. ① Regulation of glucose and lipid metabolism: studies have found that abnormal lipid metabolism can lead to obesity, diabetes, CVD and other diseases. The serum total triglyceride (TG) content in liver and serum will increase in patients with abnormal fat metabolism. The results showed that the volatile oil of galangal could increase the excretion of neutral cholesterol, significantly reduce liver TG and serum TG, and thus regulate glucose and lipid metabo?lism, prevent lipid deposition and prevent CVD. ② Improving insulin resistance (IR): inhibition of inflammatory cytokines such as IL-1, IL-6 activation and expression of TNF-α, improves IR, thereby protecting myocardium from IR-mediated damage. Through the establishment of endothelial cell injury model induced by high glucose in vitro, it was found that the volatile oil of galangal can significantly reduce the secretion of pro-inflammatory cytokines TNF-αand IL-8, and inhib?it the expression of ICAM-1 and VCAM-1 induced by high glucose, suggesting that it has protective effect on endothelial dysfunction and inflammation induced by high glucose.③Regulate blood oxygenation:during acute myocardial hypoxia, the activity of free radical scavenging system is decreased, and oxygen free radicals are produced in large quantity, which reacts with unsaturated fatty acids on the cell membrane and forms lipid peroxidation, resulting in myocardial structural damage. The results showed that the water extract of Galangal could reduce the content of MDA in blood and protect the SOD activity of ischemic and hypoxic myocardium.④ Protective effect of vascular endothelial cells (ES):ES injury is the pathological basis of some cardiovascular diseases. The results showed that the volatile oil of galangal had a protective effect on ES apoptosis. Compared with the morphology and activity of ES treated with oxidized LDL, galan?gal volatile oil could ameliorate these morphological changes and improve cell viability. ⑤ Antiplatelet agglutination:inhibit platelet aggregation and thromboxane release, improve blood circulation, and have obvious anti-thrombotic effect, which has a good effect on the treatment and prevention of cardiovascular diseases. The results showed that the volatile oil of galangal had inhibitory effect on platelet aggregation and anticoagulant effect. In conclusion, the volatile oil of galangal can be used to prevent and treat cardiovascular diseases. Based on the mechanism of CVD, this study summa?rized the role of the essential oil of Alpinia officinaruim in CVD, providing basis for the clinical application of alpiniaoffici?nalis essential oil in the prevention and treatment of CVD and the development of new drugs.

Adult ; China ; Dimethoate/analogs & derivatives* ; Farmers/statistics & numerical data* ; Female ; Humans ; Male ; Middle Aged ; Occupational Exposure ; Pesticides/adverse effects* ; Polymorphism, Single Nucleotide ; Telomerase/genetics* ; Telomere/physiology*

According to the latest research progress at home and abroad, and the domestic situation, China Diabetic Cellular and Interventional Therapy Technology Alliance forDiabetic Foot developed and issued the fifth edition of clinical guidelines for comprehensive interventional diagnosis and treatment of diabetic foot, which covers domestic evidence,references foreign evidence, and reflects the progress in China. The interpretation focuses on the updated key points.