مورد إلكتروني
Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration.
| العنوان: | Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration. |
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| المؤلفون: | Michael, Alec |
| المصدر: | Animals : an open access journal from MDPI; vol 10, iss 8, E1405; 2076-2615 |
| بيانات النشر: | eScholarship, University of California 2020-08-01 |
| تفاصيل مُضافة: | Michael, Alec Kelman, Todd Pitesky, Maurice |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | The development of antimicrobial resistance (AMR) represents a significant threat to humans and food animals. The use of antimicrobials in human and veterinary medicine may select for resistant bacteria, resulting in increased levels of AMR in these populations. As the threat presented by AMR increases, it becomes critically important to find methods for effectively interpreting minimum inhibitory concentration (MIC) tests. Currently, a wide array of techniques for analyzing these data can be found in the literature, but few guidelines for choosing among them exist. Here, we examine several quantitative techniques for analyzing the results of MIC tests and discuss and summarize various ways to model MIC data. The goal of this review is to propose important considerations for appropriate model selection given the purpose and context of the study. Approaches reviewed include mixture models, logistic regression, cumulative logistic regression, and accelerated failure time-frailty models. Important considerations in model selection include the objective of the study (e.g., modeling MIC creep vs. clinical resistance), degree of censoring in the data (e.g., heavily left/right censored vs. primarily interval censored), and consistency of testing parameters (e.g., same range of concentrations tested for a given antibiotic). |
| مصطلحات الفهرس: | AMR, MIC, accelerated failure time-frailty models, cumulative logistic regression, logistic regression, mixed effect models, mixture models, Vaccine Related, Biodefense, Prevention, Emerging Infectious Diseases, Antimicrobial Resistance, Infection, Environmental Science and Management, Zoology, Animal Production, article |
| URL: | |
| الإتاحة: | Open access content. Open access content public |
| ملاحظة: | application/pdf Animals : an open access journal from MDPI vol 10, iss 8, E1405 2076-2615 |
| أرقام أخرى: | CDLER oai:escholarship.org:ark:/13030/qt5w15h9q0 qt5w15h9q0 https://escholarship.org/uc/item/5w15h9q0 https://escholarship.org/ 1393989889 |
| المصدر المساهم: | UC MASS DIGITIZATION From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1393989889 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |