Guiqi Baizhu decoction enhances radiosensitivity in non-small cell lung cancer by inhibiting the HIF-1α/DNA-PKcs axis-mediated DNA repair.

Background: Radiotherapy is one of the main treatments for non-small cell lung cancer (NSCLC), and radiosensitivity is a determinant of its efficacy. Therefore, enhancing the radiosensitivity is of great significance to improve the clinical efficacy of non-small cell lung cancer (NSCLC).

Purpose: This study intended to investigate the radiosensitisation effect and mechanism of Guiqi Baizhu decoction (GQBZD) on non-small cell lung cancer (NSCLC) and the role of hypoxia-inducible factor-1 alpha (HIF-1α)/DNA-dependent protein kinase catalytic subunit (DNA-PKcs) axis-mediated DNA non-homologous end joining (NHEJ) repair in NSCLC radiotherapy.

The impact of X-rays on cardiac hydrometabolism and the regulatory role of AS-IV.

Background: Radiation-induced cardiac injury has emerged as a significant pathological entity, with many studies focusing on the fibrotic changes in myocardial tissue. However, these do not offer solutions for the clinical prevention and treatment of radiation-induced heart disease. Regulating hydrometabolism presents a potential therapeutic target for the management of cardiovascular diseases.

Cathepsin B: The dawn of tumor therapy.

Cathepsin B (CTSB) is a key lysosomal protease that plays a crucial role in the development of cancer. This article elucidates the relationship between CTSB and cancer from the perspectives of its structure, function, and role in tumor growth, migration, invasion, metastasis, angiogenesis and autophagy. Further, we summarized the research progress of cancer treatment related drugs targeting CTSB, as well as the potential and advantages of Traditional Chinese medicine in treating tumors by regulating the expression of CTSB.

Energy metabolism: A critical target of cardiovascular injury.

Cardiovascular diseases are the main killers threatening human health. Many studies have shown that abnormal energy metabolism plays a key role in the occurrence and development of acute and chronic cardiovascular diseases. Regulating cardiac energy metabolism is a frontier topic in the treatment of cardiovascular diseases.

Research on a learning rate with energy index in deep learning.

The stochastic gradient descent algorithm (SGD) is the main optimization solution in deep learning. The performance of SGD depends critically on how learning rates are tuned over time. In this paper, we propose a novel energy index based optimization method (EIOM) to automatically adjust the learning rate in the backpropagation.

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification.

Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices.

For non-ellipsoidal extended targets and group targets tracking (NETT and NGTT), using an ellipsoid to approximate the target extension may not be accurate enough because of the lack of shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Based on these models, an improved gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter is proposed to estimate the measurement rates, kinematic states, and extension states of the sub-objects for each extended target or target group.

Improving deep convolutional neural networks with mixed maxout units.

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace.