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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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Diagnosing Onychomycosis: What's New?

Aditya K Gupta1,2, Deanna C Hall2, Elizabeth A Cooper2

  • 1Department of Medicine, Division of Dermatology, University of Toronto School of Medicine, Toronto, ON M5S 3H2, Canada.

Journal of Fungi (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

New diagnostic methods for onychomycosis (fungal nail infection) offer faster identification than traditional techniques. While established methods have limitations, emerging visual and organism identification tools aim to improve patient assessment and management.

Keywords:
artificial intelligencediagnosismicroscopyonychomycosispolymerase chain reactionspectroscopytomography

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Area of Science:

  • Medical Mycology
  • Diagnostic Pathology
  • Dermatology

Background:

  • Established onychomycosis diagnostic methods include potassium hydroxide testing, fungal culture, and histopathological examination.
  • These traditional methods, while considered the gold standard, possess significant limitations in clinical practice.
  • Onychomycosis diagnosis requires accurate identification for effective treatment decisions.

Purpose of the Study:

  • To provide an overview of established and emerging diagnostic methods for onychomycosis.
  • To compare the advantages and disadvantages of various diagnostic techniques.
  • To inform clinicians about the utility and limitations of new diagnostic approaches.

Main Methods:

  • Review of established diagnostic techniques (KOH testing, fungal culture, histopathology).
  • Outline of newer methods including visual identification (dermoscopy, OCT, confocal microscopy, UV fluorescence) and organism identification (lateral flow, PCR, MALDI-TOF, Raman spectroscopy).
  • Exploration of artificial intelligence applications in visual diagnosis.

Main Results:

  • Visual diagnosis offers clinical evidence but may lack organism-specific data.
  • Organism identification methods aim for faster and more reliable results than fungal culture.
  • Artificial intelligence shows promise in assisting visual diagnosis of onychomycosis.

Conclusions:

  • New diagnostic techniques aim to augment, but not entirely replace, established methods.
  • Each new method presents unique advantages and disadvantages for clinical use.
  • Clinicians must critically evaluate diagnostic utility and consider patient assessment needs when selecting techniques for onychomycosis diagnosis.