Background: This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.
Methods: Radiomic features were extracted from preoperative MRI of = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe).
Background: Over a century ago, Virchow proposed that cancer represents a chronically inflamed, poorly healing wound. Normal wound healing is represented by a transitory phase of inflammation, followed by a pro-resolution phase, with prostaglandin (PGE2/PGD2)-induced 'lipid class switching' producing inflammation-quenching lipoxins (LXA4, LXB4).
Objective: We explored if lipid dysregulation in colorectal cancers (CRCs) is driven by a failure to resolve inflammation.
A healthy metabolism relies on precise regulation of anabolic and catabolic pathways. While insulin deficiency impairs anabolism, insulin resistance in obesity causes metabolic dysfunction, especially via altered brain insulin receptor (IR) activity. Density-enhanced phosphatase 1 (DEP-1) negatively modulates the IR in peripheral tissues.
View Article and Find Full Text PDFOral human papillomavirus (HPV) is associated with oropharyngeal cancer (OPC). Although OPC incidence is increasing globally, knowledge of oral HPV infection rates is limited. Here we carried out an observational epidemiological analysis of oral HPV incidence in 3,137 men enrolled from the United States, Mexico and Brazil between 2005 and 2009.
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