Cutting Edge Artificial Intelligence, Spatial Transcriptomics and Proteomics Approaches to Analyze Cancer, Volume 163 in the Advances in Cancer Research series, highlights new advances in the field, with this new volume presenting interesting topics on the Impact of thermal processing on food flavonoids, Bioinformatics and bioactive peptides from foods: does it work together?, Food off-flavor volatiles generation, characterization and advances in novel strategies for mitigating off-flavor perception, Innovations in Food Packaging for a Sustainable and Circular economy, Upcycling of seafood side streams for circularity, Edible insects in foods, Effect of novel food processing technologies on Bacillus cereus spores, and more. - Contains contributions that have been carefully selected based on their vast experience and expertise on the subject - Includes updated, in-depth, and critical discussions of available information, giving the reader a unique opportunity to learn - Encompasses a broad view of the topics at hand

The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Advances in Cancer Research, Volume 150, the latest release in this ongoing series, covers the relationship(s) between autophagy and senescence, how they are defined, and the influence of these cellular responses on tumor dormancy and disease recurrence. Specific sections in this new release include Autophagy and senescence, converging roles in pathophysiology, Cellular senescence and tumor promotion: role of the unfolded protein response, autophagy and senescence in cancer stem cells, Targeting the stress support network regulated by autophagy and senescence for cancer treatment, Autophagy and PTEN in DNA damage-induced senescence, mTOR as a senescence manipulation target: A forked road, and more. Addresses the relationship between autophagy and senescence in cancer therapy Covers autophagy and senescence in tumor dormancy Explores autophagy and senescence in disease recurrence

Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population.

This book will serve as a primer for both laboratory and field scientists who are shaping the emerging field of molecular epidemiology. Molecular epidemiology utilizes the same paradigm as traditional epidemiology but uses biological markers to identify exposure, disease or susceptibility. Schulte and Perera present the epidemiologic methods pertinent to biological markers. The book is also designed to enumerate the considerations necessary for valid field research and provide a resource on the salient and subtle features of biological indicators.

The definitive, complete reference of digital pathology! An extraordinarily comprehensive and complete book for individuals with anything from minimal knowledge to deep, accomplished experience in digital pathology. Easy to read and plainly written, Digital Pathology examines the history and technological evolution of digital pathology, from the birth of scanning technology and telepathology to three-dimensional imaging on large multi-touch displays and computer aided diagnosis. A must-have book for anyone wishing to learn more about and work in this exciting and critical information environment including pathologists, laboratory professionals, students and any other medical practitioners with a particular interest in the history and future of digital pathology. It can also be a useful reference for anyone, medical or non-medical, who have an interest in learning more about the field. Digital pathology is truly a game changer, and this book is a crucial tool for anyone wishing to know more. Subjects discussed in depth include: Static digital imaging; basics and clinical use. Digital imaging processes. Telepathology. While slide imaging. Clinical applications of whole slide imaging. Digital pathology for educational, quality improvement, research and other settings. Forensic digital imaging.

**Selected for Doody's Core Titles® 2024 in Clinical Genetics** Psychiatric Genomics presents and synthesizes available knowledge in the field of psychiatric genomics, offering methodologies to advance new research and aid clinical translation. After providing an introduction to genomics and psychiatry, international experts discuss the genomic basis of schizophrenia, bipolar disorder, depression, personality disorders, anxiety disorders, addictions, eating disorders, and sleep disorders, among other disorders. In addition, recommendations for next steps in clinical implementation and drug discovery are discussed in-depth, with chapters dedicated to pharmacogenomics and antipsychotics, antidepressants and mood stabilizers, adverse drug reactions, implementation of pharmacogenomics in psychiatric clinics, and ethical issues. Finally, methods sections provide a solid grounding in research approaches and computational analytics, from using animal models in psychiatric genomics and accessing biobanks, to employing computational analysis, genome-wide association studies (GWAS), brain pathophysiology, and endophenotypes in psychiatric research. - Thoroughly examines the genetic mechanisms underlying a broad range of psychiatric disorders - Offers genomic methodologies and analytical approaches supporting new research and clinical translation, including personalized diagnosis and treatment models - Features chapter contributions from international leaders in the field