Influence of antiviral therapy on survival of patients with hepatitis B-associated hepatocellular carcinoma undergoing transarterial chemoembolization

Purpose: To examine the prognostic value of antiviral therapy among hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE).Method: A total of 356 patients with HCC undergoing TACE were recruited for the purpose of current study. All the patients were categorized into two groups; antiviral (n = 132) and non-antiviral group (n = 224). All the clinical and laboratory parameters were noted at baseline. Patients were then followed up for five years. The mortality rates in two groups were evaluated with Kaplan-Meier estimate.Results: The average age of the participants was 51.2 ± 6.17 years. A majority (329; 92.4 %) of the patients were male while females constituted only 7.6 %. During five years follow-up period, a total of 274 (76.9 %) died, with 89 patients belonging to the antiviral group while the remaining 185 patients were in the non-antiviral group. Mortality rate significantly differed between the antiviral and non-antiviral groups (67.4 % versus 82.5 %, p = 0.028). The results of Cox regression demonstrated that being a smoker, low serum albumin, local ablation and resection decreased overall survival while female gender, antiviral therapy, and early tumor site-node-metastatis (TNM) staging increased overall survival.Conclusion: Antiviral therapy for underlying hepatitis B in HCC patients undergoing TACE prolongs overall survival and prevents or delays reactivation of tumor.Keywords: Cancer, Chemoembolization, Hepatitis, Hepatocellular carcinoma, Transarterial chemoembolization (TACE

Dynamic performance analysis for wind turbine in complex conditions

The effect of dynamic performance shall be considered when calculating the wind speed relative to the wind turbine structure, since it is essential to prolong its service life. This article presents a method to get dynamic responses of a wind turbine under different conditions. The time-varying load acting on the blade is calculated by using the blade element momentum theory, and the dynamic performance of the wind turbine are calculated by applying the modal superposition method with blade loads as excitations. A platform is constructed to experimentally test the dynamic responses of the wind turbine system. The dynamic response process is adopted to carry out a dynamic analysis, and theoretical results are compared with experimental results, indicated that the analysis presented in this paper is correct. In addition, the 2 MW wind turbine operating in different wind fields is analyzed by applying the computing method. The results indicate that the wind turbine experiences a huge transverse vibration under turbulent wind, the hub vibration is intensified up to 179.52 %, and the vibration of the blade tip intensifies up to 190.41 % under the action of gusts in extreme conditions relative to the steady state, which shall be considered during design

The Diagonally Dominant Degree and Disc Separation for the Schur Complement of Ostrowski Matrix

By applying the properties of Schur complement and some inequality techniques, some new estimates of diagonally and doubly diagonally dominant degree of the Schur complement of Ostrowski matrix are obtained, which improve the main results of Liu and Zhang (2005) and Liu et al. (2012). As an application, we present new inclusion regions for eigenvalues of the Schur complement of Ostrowski matrix. In addition, a new upper bound for the infinity norm on the inverse of the Schur complement of Ostrowski matrix is given. Finally, we give numerical examples to illustrate the theory results

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure