Efficacy and safety of polymer-free stent versus polymer-permanent drug-eluting stent in patients with acute coronary syndrome: a meta-analysis of randomized control trials.

BACKGROUND: The efficacy and safety of polymer-free stent (PFS) versus permanent polymer drug-eluting stent (PPDES) in patients undergoing percutaneous coronary intervention (PCI) remain controversial. Our meta-analysis was undertaken to evaluate and compare the efficacy and safety of PFS with those of PPDES in patients undergoing PCI. METHODS: We searched PubMed, Cochrane Library, EMBASE, and Clinical Trials.gov databases for randomized controlled trials (RCTs). The primary endpoints were incidence of stent thrombosis (ST) and target-lesion revascularization (TLR). The secondary endpoints included the incidence of major adverse cardiovascular events (MACE), myocardial infarction (MI), cardiac death (CD), late lumen loss (LLL), and diameter stenosis (DS). Subgroup analyses were also conducted based on the follow-up time. RESULTS: Eleven RCTs met the including criteria, and 8616 patients were included in the study. No significant differences were observed between PFS and PPDES treatments in the incidence of ST (RR 0.90; 95% CI: 0.62-1.31; P = 0.58; I 2  = 0), TLR (RR 0.87; 95% CI: 0.76-1.00; P = 0.05; I 2  = 37%), CD (RR 0.89; 95% CI: 0.72-1.10; P = 0.28; I 2  = 0), MI (RR 0.87; 95% CI: 0.71-1.05; P = 0.15; I 2  = 0), LLL (SMD 0.01; 95% CI: -0.29-0.30; P = 0.96; I2 = 90%), and DS (SMD -0.01; 95% CI: - 0.25 to 0.23; P = 0.93; I2 = 83%). Meanwhile, the patients with PFS had a lower incidence of MACE (RR 0.87; 95% CI: 0.78-0.97; P = 0.01; I 2  = 0) than those with PPDES. CONCLUSION: In the overall analysis, patients with PFS presented a lower risk of MACE versus PPDES, but no significant difference were obtained in the risk of ST, TLR, MI, CD, DDD and DS. In the Short term follow up, patients with PSF presented a lower risk of TLR compared with PPDES.

Left Ventricular Noncompaction Combined With Epinephrine-Secreted Pheochromocytoma Inducing Heart Failure.

Pheochromocytomas and left ventricular noncompaction (LVNC) are both rare diseases. In this patient, the long duration of the catecholamine-secreted pheochromocytoma caused myocardial ischemia, pressure overload, and hypertrophy, resulting in the onset of heart failure (HF). The LVNC might be associated with the acute attack of HF induced by the pheochromocytoma. This is the first case reporting LVNC in combination with HF secondary to pheochromocytoma.

Marine n-3 PUFA protects hearts from I/R injury via restoration of mitochondrial function.

OBJECTIVES: Overwhelming evidence shows that dietary supplementation with n-3 polyunsaturated fatty acids (PUFAs) elicits protective effects on patients with cardiovascular disease. However, the detailed mechanisms underlying n-3 PUFA-mediated cardioprotection are unknown, and examined in the present study. METHODS: We evaluated heart performances with Langendorff perfusion apparatus. Meanwhile, whole mitochondria were purified from non-perfused hearts for functional assessment, and lipid peroxidation level was measured as well. RESULTS: Compared with control groups, hearts from n-3 PUFA-supplemented rats showed improved functional recovery and reduced tissue injury following ischemia/reperfusion (I/R). Furthermore, the mitochondrial function of PUFA-treated hearts was significantly enhanced, as demonstrated by biochemical analysis of respiratory chain activity. In addition, thiobarbituric acid-reactive substance or TBARS assay revealed that lipid peroxidation product, malondialdehyde or MDA, in the mitochondria was significantly reduced by PUFA treatment. CONCLUSION: Taken together, our data indicate that marine n-3 PUFA could improve cardiac performance after I/R injury by restoring mitochondrial respiratory activities and attenuating lipid peroxidation.

Graph-based social relation inference with multi-level conditional attention.

Social relation inference intrinsically requires high-level semantic understanding. In order to accurately infer relations of persons in images, one needs not only to understand scenes and objects in images, but also to adaptively attend to important clues. Unlike prior works of classifying social relations using attention on detected objects, we propose a MUlti-level Conditional Attention (MUCA) mechanism for social relation inference, which attends to scenes, objects and human interactions based on each person pair. Then, we develop a transformer-style network to achieve the MUCA mechanism. The novel network named as Graph-based Relation Inference Transformer (i.e., GRIT) consists of two modules, i.e., a Conditional Query Module (CQM) and a Relation Attention Module (RAM). Specifically, we design a graph-based CQM to generate informative relation queries for all person pairs, which fuses local features and global context for each person pair. Moreover, we fully take advantage of transformer-style networks in RAM for multi-level attentions in classifying social relations. To our best knowledge, GRIT is the first for inferring social relations with multi-level conditional attention. GRIT is end-to-end trainable and significantly outperforms existing methods on two benchmark datasets, e.g., with performance improvement of 7.8% on PIPA and 9.6% on PISC.

Establishment and validation of a 3-month prediction model for poor functional outcomes in patients with acute cardiogenic cerebral embolism related to non-valvular atrial fibrillation.

Objectives: Cardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations. Methods: We included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables. Results: The 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model's Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854-0.902), 0.918 (95% CI, 0.857-0.979), and 0.839 (95% CI, 0.744-0.934), respectively. Conclusion: The N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings.

Effect of pH value and the distance between the electrodes on physicochemical properties of chitosan under SPP treatment.

Solution plasma process (SPP) was employed to degrade chitosan. The effects of the initial pH value and the distance between the electrodes on the steady shear flow behavior, structural characterization, molecular conformation, and antioxidant activity of chitosan were investigated. The results revealed that a lower initial pH value and a narrower distance between the electrodes were beneficial to the decrease in viscosity and increase in shear-thinning capacity. Structural characterization of the chitosan by FT-IR and 1H NMR showed that chemical structure of chitosan was not destroyed at different process parameters. The results of XRD, HPSEC-MALLS, SEM, and AFM indicated that SPP degradation clearly decreased the crystallinity, molecular weight, molecular size, and molecular aggregation of chitosan. At initial pH values of 2.8 and 5.8, the molecular weight was 27.16 and 44.25 kDa, at the distance between the electrodes of 4 and 8 mm, it was 35.88 and 66.17 kDa, respectively. The results of DPPH and hydroxyl radical scavenging assays demonstrated that a lower initial pH value and a narrower distance between the electrodes enhanced the antioxidant activity.

Learning 3D Object Templates by Quantizing Geometry and Appearance Spaces.

While 3D object-centered shape-based models are appealing in comparison with 2D viewer-centered appearance-based models for their lower model complexities and potentially better view generalizabilities, the learning and inference of 3D models has been much less studied in the recent literature due to two factors: i) the enormous complexities of 3D shapes in geometric space; and ii) the gap between 3D shapes and their appearances in images. This paper aims at tackling the two problems by studying an And-Or Tree (AoT) representation that consists of two parts: i) a geometry-AoT quantizing the geometry space, i.e. the possible compositions of 3D volumetric parts and 2D surfaces within the volumes; and ii) an appearance-AoT quantizing the appearance space, i.e. the appearance variations of those shapes in different views. In this AoT, an And-node decomposes an entity into constituent parts, and an Or-node represents alternative ways of decompositions. Thus it can express a combinatorial number of geometry and appearance configurations through small dictionaries of 3D shape primitives and 2D image primitives. In the quantized space, the problem of learning a 3D object template is transformed to a structure search problem which can be efficiently solved in a dynamic programming algorithm by maximizing the information gain. We focus on learning 3D car templates from the AoT and collect a new car dataset featuring more diverse views. The learned car templates integrate both the shape-based model and the appearance-based model to combine the benefits of both. In experiments, we show three aspects: 1) the AoT is more efficient than the frequently used octree method in space representation; 2) the learned 3D car template matches the state-of-the art performances on car detection and pose estimation in a public multi-view car dataset; and 3) in our new dataset, the learned 3D template solves the joint task of simultaneous object detection, pose/view estimation, and part localization. It can generalize over unseen views and performs better than the version 5 of the DPM model in terms of object detection and semantic part localization.