https://www.selleckchem.com/peptide/apamin.html The findings come out on the side of the influence of the context in trauma resolution.Lung cancer is a heterogeneous and complex disease with the highest incidence and mortality rate. The present study aims at defining the lung cancer phenome specificity of lipidomic profiles, screening target lipid-dependent transcriptional alternations, identifying target lipid-associated target genes, and exploring molecular mechanisms. Lung cancer-specific and lung cancer subtype-specific target lipids palmitic acid (C160) and stearic acid (C180) were found as target lipids by integrating clinical phenomics, lipidomics, and transcriptomics and exhibited antiproliferative effects in sensitive cells. The metabolism-associated gene ACSL5 or inflammation-associated gene CCL3 was identified in lung adenocarcinoma or small lung cancer cells, respectively. C160 or C180 could upregulate ACSL5 or CSF2 expression in a time- and dose-dependent pattern, and the deletion of both genes led to the insensitivity of cells. Target lipids increased the expression of PDK4 gene in different patterns and inhibited cell proliferation through alterations of intracellular energy. Thus, our data provide a new strategy to investigate the trans-points between clinical and phenomics and lipidomics and target lipid-associated molecular mechanisms to benefit from the discovery of new therapies.PURPOSE Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for instances where patients may present for emergency services without the ability to provide this identifying information. METHODS We used the CheXpert dataset of 224,316 chest radiographs from 65,240 patients to train CNN regression models with ResNet50 and DenseNet121 architectures for prediction of patient age based on anterior