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Early identification of hereditary cancer predisposition in adolescents and young adults represents a unique opportunity to target cancer prevention and improve survival in a population at risk for adverse health outcomes. However, adolescents and young adults face challenges unique to their stage of life that can undermine their transition from pediatric to adult healthcare and lead to interruptions in preventative care. The purpose of this study was to understand expert perspectives on factors relevant to designing and implementing a transition clinic for adolescents and young adults with hereditary cancer predisposition.
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The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis.
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The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.
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