"Recently, there has been an upsurge in microbial infections. Extensive and inappropriate usage of antimicrobial drugs in treating infections has led to the evolution of a resistant strain of microorganisms and irreversible immunosuppression in humans. Medical institutions and hospitals require solutions to combat these contagions in order to avoid future epidemics. Strategies to Overcome Superbug Invasions: Emerging Research and Opportunities highlights current research and potential strategies to prevent the emergence and re-emergence of drug-resistant pathogenic microbial strains. The content within this publication examines biosensing, global initiatives, nanomaterials, and alternative therapies. It is designed for microbiologists, biotechnologists, pharmacists, pharmacologists, virologists, formulation scientists, infectious disease specialists, government officials, policymakers, healthcare practitioners, doctors, nurses, hospital directors, researchers, surgeons, and academicians who are seeking research on innovative solutions for multi-drug-resistant infections."--.

• Cover
• Title Page
• Copyright Page
• Book Series
• Editorial Advisory Board
• Table of Contents
• Foreword
• Preface
• Acknowledgment
• Section 1: Challenges and Antimicrobial Resistance (AMR): A Diagnostic Perspective
  • Chapter 1: Resistance Phenotypes and Surveillance
  • Chapter 2: Emerging Strategies for Sensing of Blood-Stream Bacterial Diseases
  • Chapter 3: Exploring the Potential of Peptides and Peptidomimetics in Biosensing
  • Chapter 4: Advances in Clinical Diagnosis of Tuberculosis
  • Chapter 5: Extra-Pulmonary TB
• Section 2: Countermeasures and Strategies for Remediation of AMR
  • Chapter 6: Traditional Medicine
  • Chapter 7: Natural Products in the Fight Against Multi-Drug-Resistant Bacteria
  • Chapter 8: Alternative Therapies
  • Chapter 9: Ancient Pediocin to Innovative Antimicrobial
  • Chapter 10: Nanomaterials to Overcome Emergence and Re-Emergence of Superbugs
  • Chapter 11: Harnessing the Capability of CADD Methods in the Prediction of Anti-COVID Drug Likeliness
• Related Readings
• About the Contributors
• Index