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Antimicrobial Effectiveness01:28

Antimicrobial Effectiveness

The effectiveness of antimicrobial agents depends on various factors influencing their ability to eliminate microbial populations. Larger microbial populations require more time for complete eradication, emphasizing the importance of population size analysis when evaluating antimicrobial efficacy.Microbial resistance to antimicrobial agents varies significantly. Highly resilient microorganisms include endospores, gram-negative bacteria, and non-enveloped viruses, while prions are exceptionally...
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Radiation and filtration are essential tools for microbial control, targeting microorganisms through distinct mechanisms. Radiation eliminates microbes by damaging their DNA, either killing them or inhibiting their growth. Based on wavelength, radiation is classified into two types: nonionizing and ionizing radiation.Non-ionizing radiation, such as UV radiation (200–400 nm), is absorbed by DNA, causing defects that effectively disinfect surfaces, air, and water, including safety cabinets.

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Related Experiment Video

Updated: Jun 3, 2025

Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics

Published on: July 19, 2024

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Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence.

Alfonso Monaco1, Mario Caruso2, Loredana Bellantuono3

  • 1Università degli Studi di Bari Aldo Moro, Dipartimento Interateneo di Fisica M. Merlin, Bari, 70125, Italy; Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, 70125, Italy.

Environmental Pollution (Barking, Essex : 1987)
|January 9, 2025
PubMed
Summary

Antimicrobial resistance (AMR) is a global health threat. Our study found water accessibility and quality are key factors influencing AMR-related mortality, offering insights for decision support.

Keywords:
Antimicrobial resistanceExplainable artificial intelligenceMachine learningSHAPWater quality

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Area of Science:

  • Environmental Science
  • Public Health
  • Infectious Diseases

Background:

  • Antimicrobial resistance (AMR) poses a significant global challenge to health, society, and the environment.
  • Pathogen resistance to antimicrobial drugs complicates infection treatment, increasing disease spread and mortality.
  • Addressing AMR requires understanding diverse contextual factors influencing its onset.

Purpose of the Study:

  • To identify key factors driving antimicrobial resistance onset in varied territorial contexts.
  • To predict country-level mortality from specific resistant pathogens using the One Health framework.
  • To leverage explainable artificial intelligence (XAI) for identifying AMR determinants.

Main Methods:

  • Utilized the explainable artificial intelligence (XAI) paradigm.
  • Employed a comprehensive set of indicators from the One Health framework.
  • Analyzed country-level data to predict mortality from resistant *Acinetobacter baumannii*, *Escherichia coli*, *Klebsiella pneumoniae*, *Pseudomonas aeruginosa*, and *Streptococcus pneumoniae*.

Main Results:

  • Identified significant correlations between water accessibility and quality indicators and AMR-related mortality.
  • Highlighted the crucial role of water-related factors in determining mortality across diverse countries.
  • Revealed the outstanding importance of water accessibility and quality in AMR mortality.

Conclusions:

  • Water accessibility and quality are critical determinants of antimicrobial resistance mortality.
  • Findings offer potential for decision support tools and monitoring systems for AMR.
  • Emphasizes the interconnectedness of water, health, and antimicrobial resistance within the One Health approach.