Jenningsnash6895

Cancer cells deploy several glucose transport protein (GLUT) channels on the cell membranes to increase glucose uptake. Cancer cells die within 24 h in the absence of glucose. Thus, preventing the deployment of GLUT channels can deprive them of glucose, resulting in apoptosis within 24 h. Herein, we developed the ID-Checker with a glucose tag that ensures its highly specific macroscale delivery of anticancer agents to the cancer cells through the GLUT channels. ID-Checker presented here showed IC50 values of 0.17-0.27 and 3.34 μM in cancer and normal cell lines, respectively. ID-Checker showed a selectivity index of 12.5-20.2, which is about 10-20 times higher than that of known anticancer agents such as colchicine. ID-Checker inhibits the microtubule formation, which results in the prevention of the deployment of GLUT channels in 6 h and kills the cancer cells within 24 h without any damage to normal cells.Left ventricular pseudoaneurysm is a rare disease; it is defined as a ventricular rupture contained by epicardium, pericardial adhesions, or both. It most frequently occurs as a complication of acute myocardial infarction. Surgical treatment is recommended for pseudoaneurysms that are large or symptomatic and for those discovered less than 3 months after myocardial infarction. We report our experience with 2 patients who had left ventricular pseudoaneurysms discovered less than a week after inferior myocardial infarction. Both patients were middle-aged men with right coronary occlusion in whom the diagnoses were established by echocardiography during the first week after infarction. Because both patients were clinically stable, we opted to defer surgery until scarring could facilitate correction; this decision was based on a review of the literature showing that in-hospital mortality is higher with early surgery. The patients were monitored closely in the intensive care unit and were prescribed β-blockers and vasodilators. Both patients underwent left ventricular patch reconstruction with exclusion of the pseudoaneurysm and posterior septum; both received moderate inotropic support and prophylactic intra-aortic balloon pump assistance. Their postoperative courses were uneventful. In 5 prior reports describing 45 patients (13 with acute pseudoaneurysm [≤2 wk after infarction] and 32 with nonacute pseudoaneurysm), in-hospital mortality was 61.5% for patients in the acute group and 15.6% for the nonacute group (P = .0066). We recommend that clinicians consider deferring surgery for patients with stable acute left ventricular pseudoaneurysm to reduce the risks associated with early repair.

A unilateral, lightweight powered hip exoskeleton has been shown to improve walking economy in individuals with above-knee amputations. However, the mechanism responsible for this improvement is unknown. In this study we assess the biomechanics of individuals with above-knee amputations walking with and without a unilateral, lightweight powered hip exoskeleton. We hypothesize that assisting the residual limb will reduce the net residual hip energy.

Eight individuals with above-knee amputations walked on a treadmill at 1 m/s with and without a unilateral powered hip exoskeleton. Flexion/extension assistance was provided to the residual hip. Motion capture and inverse dynamic analysis were performed to assess gait kinematics, kinetics, center of mass, and center of pressure.

The net energy at the residual hip decreased from 0.05±0.04 J/kg without the exoskeleton to -0.01±0.05 J/kg with the exoskeleton (p = 0.026). The cumulative positive energy of the residual hip decreased on average by 18.2% with 95% coreviously observed.Achieving accurate retinal vessel segmentation is critical in the progression and diagnosis of vision-threatening diseases such as diabetic retinopathy and age-related macular degeneration. Existing vessel segmentation methods are based on encoder-decoder architectures, which frequently fail to take into account the retinal vessel structure's context in their analysis. As a result, such methods have difficulty bridging the semantic gap between encoder and decoder characteristics. This paper proposes a Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) to address these issues by using prompt blocks. Each prompt block use combination of asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions to extract useful features. This novel strategy improves the performance of the segmentation network while simultaneously decreasing the number of trainable parameters. Our method outperformed competing approaches in the literature on three benchmark datasets, including DRIVE, STARE, and CHASE.Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https//github.com/aL3x-O-o-Hung/CAT-Net.In this article, the sliding mode control problem is addressed for a class of sampled-data systems subject to deception attacks. The sampling periods undergo component-wise random perturbations that are governed by a Markovian chain. The component of the sampled output is transmitted via an individual communication channel that is vulnerable to deception attacks, and Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the deception attacks initiated by the adversaries. A sliding mode controller is designed to drive the state into the sliding domain around the specified sliding surface, and sufficient conditions are derived to guarantee the exponentially ultimate boundedness of the resultant closed-loop system in the mean-square sense. Furthermore, an optimization problem is established to pursue locally optimal control performance. Finally, a simulation example is given to verify the effectiveness and advantages of the developed controller design approach.Subspace learning (SL) plays a key role in various learning tasks, especially those with a huge feature space. When processing multiple high-dimensional learning tasks simultaneously, it is of great importance to make use of the subspace extracted from some tasks to help learn others, so that the learning performance of all tasks can be enhanced together. To achieve this goal, it is crucial to answer the following question How can the commonality among different learning tasks and, of equal importance, the individuality of each single learning task, be characterized and extracted from the given datasets, so as to benefit the subsequent learning, for example, classification? Existing multitask SL methods usually focused on the commonality among the given tasks, while neglecting the individuality of the learning tasks. In order to offer a more general and comprehensive framework for multitask SL, in this article, we propose a novel method dubbed commonality and individuality-based SL (CISL). First, we formally define the notions and objective functions of both commonality and individuality with respect to multiple SL tasks. Then, we design an iterative algorithm to solve the formulated objective functions, with the convergence of the algorithm being guaranteed. To show the generality of the proposed method, we theoretically analyze its connections to existing single-task and multitask SL methods. Finally, we demonstrate the necessity and effectiveness of incorporating both commonality and individuality by interpreting the learned subspaces and comparing the performance of CISL (in terms of the subsequent classification accuracy) with that of classical and state-of-the-art SL approaches on both synthetic and real-world multitask datasets. The empirical evaluation validates the effectiveness of the proposed method in characterizing the commonality and individuality for multitask SL.Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. selleck kinase inhibitor Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task.