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Related Concept Videos

Development of Antibiotic Resistance01:30

Development of Antibiotic Resistance

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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Antimicrobial Effectiveness01:28

Antimicrobial Effectiveness

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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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Antibiotic Selection00:57

Antibiotic Selection

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Updated: Dec 6, 2025

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
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Anomalous Antimicrobial Susceptibility Trend Identification.

M L Tlachac, Elke Rundensteiner, Kerri Barton

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
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    Summary

    This study introduces a new method to identify unusual antibiotic resistance trends in bacteria, improving the accuracy of future predictions. This helps in better antibiotic prescribing to combat resistance.

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    Area of Science:

    • Infectious Diseases
    • Medical Informatics
    • Computational Biology

    Background:

    • Antibiotic-resistant bacterial infections pose a significant global health threat.
    • Antibiograms are essential for monitoring antimicrobial resistance and guiding antibiotic selection.
    • Current antibiogram data are often delayed due to lengthy annual compilation processes.

    Purpose of the Study:

    • To improve the timeliness and accuracy of antimicrobial susceptibility forecasting.
    • To identify antibiotic-bacteria combinations with anomalous resistance trends that hinder accurate forecasting.
    • To develop a strategy for removing these anomalous trends before forecasting.

    Main Methods:

    • Utilized a 15-year Massachusetts statewide antibiogram dataset.
    • Developed the Previous Year Anomalous Trend Identification (PYATI) strategy.
    • Employed a cluster-driven outlier detection method within PYATI to identify anomalous trends.

    Main Results:

    • PYATI effectively identifies antibiotic-bacteria combinations with anomalous resistance trends.
    • Removing these anomalous trends significantly reduces forecasting errors for remaining combinations.
    • The strategy enhances the statistical reliability of antimicrobial susceptibility predictions.

    Conclusions:

    • The PYATI strategy offers a novel approach to refine antimicrobial resistance forecasting.
    • By addressing anomalous trends, forecasting accuracy is statistically improved.
    • This method can aid in optimizing antibiotic prescribing practices to combat resistance.