Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Vesicle Trafficking in Cancer
Endocytosis and vesicular trafficking determine the landscape of the cell’s exterior, namely the density of surface molecules, such as receptors for growth factors and cytokines, adhesion molecules like integrins and cadherins, and a plethora of nutrient carriers. Hence, endocytosis is involved in signal transduction, cell adhesion and migration, as well as metabolism. To exploit these fundamental processes, malignancies subtly and multiply manipulate the endocytosis and the subsequent trafficking of protein cargoes. This is achieved by simultaneously altering the cytoskeleton, vesicle budding, cargo sorting and intracellular degradation. By highlighting the underlying molecular processes and concentrating on specific examples, this book reviews the recent emergence of derailed endocytosis and vesicular trafficking as a landmark of cancer. In-depth understanding of this common feature of tumors might lead the way to drug-induced strategies, able to rectify intracellular trafficking in cancer.
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.
AACR 2022 Proceedings: Part B April 11-13
The AACR Annual Meeting is the focal point of the cancer research community, where scientists, clinicians, other health care professionals, survivors, patients, and advocates gather to share the latest advances in cancer science and medicine. From population science and prevention; to cancer biology, translational, and clinical studies; to survivorship and advocacy; the AACR Annual Meeting highlights the work of the best minds in cancer research from institutions all over the world.
AACR 2022 Proceedings: Part A Online-Only and April 10
The AACR Annual Meeting is the focal point of the cancer research community, where scientists, clinicians, other health care professionals, survivors, patients, and advocates gather to share the latest advances in cancer science and medicine. From population science and prevention; to cancer biology, translational, and clinical studies; to survivorship and advocacy; the AACR Annual Meeting highlights the work of the best minds in cancer research from institutions all over the world.
Deep Learning for Cancer Diagnosis
This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
