BackgroundHeart failure has become a global health problem with increasing incidences worldwide. Traditional pharmacological treatments can delay but cannot reverse the underlying disease processes. The clinical application of myocardial tissue engineering represents a promising strategy because it features cell-based replacement therapies that replace partially or fully damaged cardiac tissues with in vitro-generated tissue equivalents. However, the effectiveness of this therapy is limited by poor viability and differentiation of the grafted cells. This limitation could be overcome by rapidly increasing the numbers of functional cardiomyocytes. In this study, we aimed to obtain functional myocardial tissue engineering seed cells with high proliferation and differentiation rates by combining 1,2-dimyristoyl-sn-glycero-3-phosphoethan-olamine-polyethylene glycol (DMPE-PEG) and recombinant transforming growth factor-β1 receptor I (rTGF-β1 RI), followed by binding to human adipose-derived stromal cells (hADSCs).MethodsTo induce higher expression level of TGF-β1 RI, DMPE-PEG was inoculated with rTGF-β1 RI to modify the surface of hADSCs. The differentiation ability and morphological characteristics of the modified hADSCs were examined in vitro and in vivo.ResultsThe caridiomyocartic differentiation ability of TGF-β1 RI-modified hADSCs was significantly enhanced, as indicated by elevated expression levels of the cardiac markers cardiac troponin T (cTnT) and α-smooth muscle actin (SMA) via increased phosphorylation of the Smad signaling pathway-related proteins.ConclusionOur findings provide new insights into stem cell transplantation therapy in myocardial tissue engineering.
Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.
This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group (received CLSRR) and a control group (received assisted laparoscopic radical resection (ALSRR)), with 39 cases in each group. It was found that the BPS algorithm showed lower relative mean square error (MSE) in axle x (OMSE, x) and axle y (OMSE, x), but the classification accuracy (CA) was the opposite (
P
<
0.05
). The postoperative hospital stay, analgesia duration, first exhaust time (FET), and first off-bed activity time (FOBA) for patients in the experimental group were less (
P
<
0.05
). The operation time of the experimental group (270.56 ± 90.55 min) was significantly longer than that of the control group (228.07 ± 75.26 min) (
P
<
0.05
). There were 3 cases of anastomotic fistula, 1 case of acute peritonitis, and 2 cases of lung infections in the experimental group, which were greatly less than those in the control group (7 cases, 4 cases, and 3 cases) (
P
<
0.05
). In short, the BPS algorithm was superior in processing MRI images and could improve the diagnostic effect of MRI images. The CLSRR could reduce the length of hospital stay and the probability of complications in GC patients, so it could be used as a surgical plan for the clinical treatment of advanced GC.
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