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Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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High-throughput Confocal Imaging of Quantum Dot-Conjugated SARS-CoV-2 Spike Trimers to Track Binding and Endocytosis in HEK293T Cells
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Photonics enabled intelligence system to identify SARS-CoV 2 mutations.

Bakr Ahmed Taha1, Qussay Al-Jubouri2, Yousif Al Mashhadany3

  • 1UKM-Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Malaysia.

Applied Microbiology and Biotechnology
|April 28, 2022
PubMed
Summary

This study proposes an AI-powered photonics framework for rapid SARS-CoV-2 mutation detection. It analyzes Omicron variant spread and spike protein mutations, enabling early identification for future outbreak prevention.

Keywords:
COVID-19 variantIntelligence systemMutationsPhotonicSARS-CoV 2Spike protein

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Area of Science:

  • Virology
  • Biophotonics
  • Artificial Intelligence

Background:

  • The ongoing spread of SARS-CoV-2, causing COVID-19, poses a significant global health threat.
  • Viral mutations, like those observed in the Omicron variant, alter characteristics and necessitate continuous monitoring.
  • Previous epidemics like MERS-CoV and SARS-CoV highlight the danger of coronaviruses.

Purpose of the Study:

  • To develop an early identification system for SARS-CoV-2 mutations using photonics and AI.
  • To compare the spread rates of the Omicron variant with other SARS-CoV-2 variants globally.
  • To identify critical mutation areas within the SARS-CoV-2 spike protein.

Main Methods:

  • A novel framework combining photonics and artificial intelligence (AI) for SARS-CoV-2 mutation identification.
  • Analysis of global data to compare the spread rates of different SARS-CoV-2 variants, including Omicron.
  • Evaluation of mutation sites within the viral spike protein.

Main Results:

  • A proposed AI-enabled photonics framework for identifying and sorting SARS-CoV-2 mutations.
  • Comparative analysis of Omicron variant scatter rates against other SARS-CoV-2 variants across countries.
  • Identification of key mutation areas in the spike protein.

Conclusions:

  • Photonics approaches, enhanced by AI, offer a direct method for SARS-CoV-2 detection without sample preparation.
  • This technology can be integrated into the Internet of Healthcare Things for improved public health surveillance.
  • Early detection of SARS-CoV-2 mutations is crucial for planning and preventing future epidemics.