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A mixed-effects deep learning model to diagnose COVID-19 from computed tomography imaging

Artificial intelligence for predicting TMB-H colorectal cancer from histopathological characteristics

Artificial intelligence for predicting TMB-H colorectal cancer from histopathological characteristics Biomarkers are important determinants of appropriate and effective therapeutic approaches for various diseases including cancer. There is ample evidence pointing toward the significance of immune check point inhibitors (ICI) against cancer, and they showed promising clinical benefits to a specific group of patients with colorectal cancer (CRC). Several reports demonstrated the efficacy of biomarkers such as programmed death-1 protein ligand (PD-L1), density of tumor-infiltrating lymphocytes (TILs), and tumor mutational burden (TMB), to determine the patient responsiveness for the efficient use of ICIs as therapeutics against cancer. A high level of TMB (TMB-H), which reflects elevated total number of non-synonymous somatic mutations per coding area of a tumor genome and normally derived from gene panel testing, is recognized as a promising biomarker for the ICI therapies of various

Researchers Use AI System To Identify Mosquitoes

Researchers use AI to identify mosquitoes. Pixabay Researchers have shown the effectiveness of an artificial intelligence (AI) system known as a Convoluted Neural Network to classify mosquito sex, genus, species, and strain. Rapid and accurate identification of mosquitoes that transmit human pathogens such as malaria is an essential part of mosquito-borne disease surveillance, the study published in PLOS Neglected Tropical Diseases, reported. Human malaria is an ongoing public health crisis affecting many continents, with the highest numbers of cases and people at risk in sub-Saharan Africa. However, the identification of mosquitoes that transmit malaria can be difficult some species are nearly indistinguishable even to trained taxonomists.

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