Self Paced

CRISPR-Cas12/Cas13 for Rapid Pathogen Detection

Revolutionizing Pathogen Detection with Precision CRISPR Technology

Register NowExplore Details

Early access to e-LMS included

  • Mode: Online/ e-LMS
  • Type: Self Paced
  • Level: Moderate
  • Duration: 4 weeks

About This Course

CRISPR-Cas12/Cas13 for Rapid Pathogen Detection is a transformative one-month program that delves into the application of CRISPR technology beyond genome editing, focusing specifically on pathogen detection. Participants will start with a foundational understanding of the CRISPR-Cas systems, including the biochemical mechanisms that enable these proteins to detect nucleic acids.

Aim

This course is designed to equip participants with the cutting-edge CRISPR-Cas12 and Cas13 technologies for rapid pathogen detection. By the end of the program, participants will be proficient in applying these tools in clinical and environmental settings to quickly identify and control infectious diseases and outbreaks.

Program Objectives

  • Understand the fundamentals of CRISPR-Cas12 and Cas13 mechanisms.
  • Develop skills to design and implement CRISPR-based diagnostics.
  • Apply CRISPR technology for rapid detection of pathogens in various samples.
  • Interpret diagnostic results for effective disease management and outbreak control.
  • Promote innovation in the field of molecular diagnostics through advanced CRISPR applications.

Program Structure

Week 1: Fundamentals of CRISPR-Cas Systems

  • Overview of CRISPR technology and its mechanisms
  • Differences between Cas12 and Cas13 enzymes
  • Introduction to applications of CRISPR in diagnostics

Week 2: CRISPR-Cas for Pathogen Detection

  • Mechanism of CRISPR-based pathogen detection
  • Key techniques for using Cas12/Cas13 in identifying viral and bacterial pathogens
  • Understanding sensitivity, specificity, and accuracy in CRISPR diagnostics

Week 3: Workflow and Sample Preparation for CRISPR Diagnostics

  • Sample collection and preparation for CRISPR-based detection
  • Developing a workflow for rapid and efficient pathogen detection
  • Optimizing reaction conditions and troubleshooting common issues

Week 4: Practical Applications and Future Developments

  • Case studies on CRISPR diagnostics in infectious disease detection
  • Future trends and advancements in CRISPR-based diagnostics
  • Ethical considerations and regulatory challenges in CRISPR pathogen detection

Who Should Enrol?

  • Undergraduate degree in Molecular Biology, Biotechnology, Genetics, or related fields.
  • Professionals in the biomedical, healthcare, or environmental science sectors.
  • Individuals interested in the latest biotechnological advances and their applications in disease control and public health.

Program Outcomes

  • Mastery of CRISPR-based diagnostic technologies.
  • Ability to design and conduct pathogen detection assays.
  • Proficiency in interpreting and reporting diagnostic results.
  • Skills in troubleshooting and optimizing CRISPR diagnostic assays.
  • Capability to implement CRISPR diagnostics in diverse settings.

Fee Structure

Standard: ₹8,998 | $198

Discounted: ₹4499 | $99

We accept 20+ global currencies. View list →

What You’ll Gain

  • Full access to e-LMS
  • Real-world dry lab projects
  • 1:1 project guidance
  • Publication opportunity
  • Self-assessment & final exam
  • e-Certificate & e-Marksheet

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Publication Opportunity

Get published in a prestigious open-access journal.

Centre of Excellence

Become part of an elite research community.

Networking & Learning

Connect with global researchers and mentors.

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Need Help?

We’re here for you!


(+91) 120-4781-217

★★★★★
Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program

Thank you

Rabea Ghandour
★★★★★
AI for Environmental Monitoring and Sustainablility

Menthor was easy to follow

IVANA PILJEK MILETIĆ
★★★★★
Build Intelligent AI Apps with Retrieval-Augmented Generation (RAG)

None

Alexandros Karakikes
★★★★★
Large Language Models (LLMs) and Generative AI

The mentor was supportive, clear in their guidance, and encouraged active participation throughout the process.

António Ricardo de Bastos Teixeira

View All Feedbacks →

Still have any Query?