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Cancer Nanotechnology
Cancer nanotechnology is a growing, emerging area of cross-disciplinary research that aims to develop efficient, specific and noninvasive approaches to restore the health and well-being of all cancer patients through more effective diagnosis and treatment. This new volume serves as a fundamental guide to cutting-edge topics in cancer nanotechnology, including advances in therapy, the use of nanoparticles and nanomaterials, future directions for nanocarriers in cancer therapy, and the application of DNA and RNA nanovaccines. Organized into four sections, the volume presents an overview of research and innovation in the emerging field of nanotechnology as a powerful tool in the diagnosis, imaging and treatment of cancer. International experts author chapters addressing targets of cancer therapy, materials for cancer nanotechnology, strategies for cancer therapy using nanotechnology, and innovative nanotechnologies for cancer diagnosis and treatment. The volume will be useful for a broad audience, including cross-disciplinary researchers, trainees, health professionals, and experts in industry.
Artificial intelligence in predicting, determining and controlling cell phenotype or tissue function in inflammatory diseases
The last decade has seen rapid development in the use of computational techniques at bulk tissue and single-cell level. However, our knowledge remains limited in this regard, and further progress is needed, especially in inflammatory and degenerative diseases. Controlling, modeling, or predicting cellular phenotype in this context using artificial intelligence (AI) will greatly improve the available in vitro, in situ, in vivo, and in silico methods, but also aid in the understanding of disease pathology and therapeutic efficiency. These methods not only have ramifications for our pathophysiological understanding of tissue function but are also important for advancing AI methods in cell culture, tissue explants, or in vivo for immunologically relevant characteristics of single cells, cell populations, and tissues to predict cell or tissue function.
