Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020.

Background: Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors. Methods: We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis. Results: The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001. Conclusion: The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

A method of motion estimation of segmental ventricular wall with tracking of ultrasonic echocardiogram.

BACKGROUND: Ultrasonic echocardiography is commonly used for monitoring myocardial dysfunction. However, it has limitations such as poor quality of echocardiography images and subjective judgment of doctors. METHODS: In this paper, a calculation model based on optical flow tracking of echocardiogram is proposed for the quantitative estimation motion of the segmental wall. To improve the accuracy of optical flow estimation, a method based on confidence-optimized multiresolution(COM) optical flow model is proposed to reduce the estimation errors caused by the large amplitude of myocardial motion and the presence of "shadows" and other image quality problems. In addition, motion vector decomposition and dynamic tracking of the ventricular region of interest are used to extract information regarding the myocardial segmental motion. The proposed method was validated using simulation images and 50 clinical cases (25 patients and 25 healthy volunteers) for myocardial motion analysis. RESULTS: The results demonstrated that the proposed method could track the motion information of myocardial segments well and reduce the estimation errors of optical flow caused due to the use of low-quality echocardiogram images. CONCLUSIONS: The proposed method improves the accuracy of motion estimation for the cardiac ventricular wall.

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.

An anti-CTLA-4 heavy chain-only antibody with enhanced Treg depletion shows excellent preclinical efficacy and safety profile.

The value of anti-CTLA-4 antibodies in cancer therapy is well established. However, the broad application of currently available anti-CTLA-4 therapeutic antibodies is hampered by their narrow therapeutic index. It is therefore challenging and attractive to develop the next generation of anti-CTLA-4 therapeutics with improved safety and efficacy. To this end, we generated fully human heavy chain-only antibodies (HCAbs) against CTLA-4. The hIgG1 Fc domain of the top candidate, HCAb 4003-1, was further engineered to enhance its regulatory T (Treg) cell depletion effect and to decrease its half-life, resulting in HCAb 4003-2. We tested these HCAbs in in vitro and in vivo experiments in comparison with ipilimumab and other anti-CTLA4 antibodies. The results show that human HCAb 4003-2 binds human CTLA-4 with high affinity and potently blocks the binding of B7-1 (CD80) and B7-2 (CD86) to CTLA-4. The results also show efficient tumor penetration. HCAb 4003-2 exhibits enhanced antibody-dependent cellular cytotoxicity function, lower serum exposure, and more potent anti-tumor activity than ipilimumab in murine tumor models, which is partly driven by a substantial depletion of intratumoral Tregs. Importantly, the enhanced efficacy combined with the shorter serum half-life and less systemic drug exposure in vivo potentially provides an improved therapeutic window in cynomolgus monkeys and preliminary clinical applications. With its augmented efficacy via Treg depletion and improved safety profile, HCAb 4003-2 is a promising candidate for the development of next generation anti-CTLA-4 therapy.

Pseudidiomarina donghaiensis sp. nov. and Pseudidiomarina maritima sp. nov., isolated from the East China Sea.

Two Gram-negative, aerobic, motile, rod-shaped bacteria, designated strains 908033(T) and 908087(T), were isolated from a seawater sample collected from the East China Sea. Chemotaxonomic characteristics of the two isolates included the presence of iso-C(15 : 0), iso-C(17 : 0) and iso-C(17 : 1)omega9c as the major cellular fatty acids and Q-8 as the predominant ubiquinone. The genomic DNA G+C contents of strains 908033(T) and 908087(T) were 45.5 and 45.2 mol%, respectively. Phylogenetic analyses based on 16S rRNA gene sequences revealed that the new isolates were related to members of the genus Pseudidiomarina, showing levels of similarity of 95.8-96.6 % with the type strains of recognized species of the genus. The results of DNA-DNA hybridization experiments among these two isolates and Pseudidiomarina sediminum CICC 10319(T), in combination with chemotaxonomic and phenotypic data, demonstrated that the new isolates represent two novel species of the genus Pseudidiomarina, for which the names Pseudidiomarina donghaiensis sp. nov. (type strain 908033(T)=CGMCC 1.7284(T)=JCM 15533(T)) and Pseudidiomarina maritima sp. nov. (type strain 908087(T)=CGMCC 1.7285(T)=JCM 15534(T)) are proposed.