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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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In-Field Detection of Plant Pathogens Using Three-Dimensional-Printed Microneedles and a Portable Platform.

Emre Ece1,2, Nedim Hacıosmanoğlu1,2, Murat Alp Güngen1,2

  • 1UNAM-National Nanotechnology Research Center, Bilkent University, 06800 Ankara, Turkey.

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Summary
This summary is machine-generated.

A new microneedle (MN) diagnostic platform offers rapid, field-deployable plant disease detection. This system efficiently samples pathogens for early diagnosis, ensuring food security.

Keywords:
LAMPLFAmicroneedle-based diagnosticsplant pathogen detectionsustainable agriculture

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Area of Science:

  • Agricultural Science
  • Biotechnology
  • Plant Pathology

Background:

  • Plant diseases pose a significant threat to global food security.
  • Current diagnostic methods are often unreliable for early-stage infections and subsurface pathogens.

Purpose of the Study:

  • To develop and evaluate a novel microneedle (MN)-based diagnostic platform for early plant disease detection.
  • To assess the efficacy of 3D-printed MNs compared to traditional sampling methods.

Main Methods:

  • Integration of microneedles (MNs) with loop-mediated isothermal amplification (LAMP) and lateral flow assay (LFA).
  • Development of a portable heating device (LAMPbox) for field-deployable diagnostics.
  • Evaluation of poly(vinyl alcohol) (PVA) and 3D-printed MNs for pathogen sampling and DNA extraction.

Main Results:

  • 3D-printed MNs demonstrated superior mechanical robustness and higher DNA yield compared to PVA MNs and swab-based methods.
  • The platform successfully detected *Puccinia triticina* in plant leaves, differentiating between healthy and infected samples.
  • The system proved effective for early plant disease diagnosis, even in the absence of visible symptoms.

Conclusions:

  • 3D-printed MNs are effective and robust tools for plant pathogen sampling.
  • The developed low-cost, portable diagnostic system is feasible for sustainable, early plant disease detection.
  • This technology has the potential to significantly improve agricultural monitoring and disease management.