A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning has become the main method for lung nodule classification. However, successful deep learning models usually require large number of parameters and carefully annotated data.

The global landscape of immune-derived lncRNA signature in colorectal cancer.

Background: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity.

The gut microbial metabolite trimethylamine N-oxide and cardiovascular diseases.

Morbidity and mortality of cardiovascular diseases (CVDs) are exceedingly high worldwide. Researchers have found that the occurrence and development of CVDs are closely related to intestinal microecology. Imbalances in intestinal microecology caused by changes in the composition of the intestinal microbiota will eventually alter intestinal metabolites, thus transforming the host physiological state from healthy mode to pathological mode.

Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.

With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules.