Chung-Ming Chang – författare

Nanotherapeutics for Inflammatory Arthritis: Design, Diagnosis, and Treatment highlights nanobiotechnology and its therapeutic applications in the field of inflammatory arthritis, the interaction of nanomaterials in the biological systems, and clinical development of nanomedicines. It also covers the discovery of personalized therapeutics, diagnostics, and nanoparticular delivery systems, the role of bioinformatics nanobiotechnology in personalized oncology. The use of nanosensors for the detection and current challenges in the development of personalized medicine is explained including recent nanotechnology-based strategies.

Features:

This book is aimed at researchers and professionals in nanotechnology, biomaterial, drug delivery, and inflammatory arthritis.

Nanotherapeutics for Inflammatory Arthritis: Design, Diagnosis, and Treatment highlights nanobiotechnology and its therapeutic applications in the field of inflammatory arthritis, the interaction of nanomaterials in the biological systems, and clinical development of nanomedicines. It also covers the discovery of personalized therapeutics, diagnostics, and nanoparticular delivery systems, the role of bioinformatics nanobiotechnology in personalized oncology. The use of nanosensors for the detection and current challenges in the development of personalized medicine is explained including recent nanotechnology-based strategies.

Features:

This book is aimed at researchers and professionals in nanotechnology, biomaterial, drug delivery, and inflammatory arthritis.

This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.

This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine.

This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.

This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine.