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Reproducibility of antimicrobial test methods.

ABSTRACT: We review reproducibility results for methods that test antimicrobial efficacy against biofilms, spores and bacteria dried onto a surface. Our review, that included test results for Pseudomonas aeruginosa, Salmonella choleraesuis and Bacillus subtilis, suggests that the level of reproducibility depends on the efficacy of the antimicrobial agent being tested for each microbe and microbial environment. To determine the reproducibility of a method, several laboratories must independently test the same antimicrobial agent using the method. Little variability among the efficacy results suggests good reproducibility. Such reproducibility assessments currently are hampered by the absence of an objective process for deciding whether the variability is sufficiently small. We present a quantitative decision process that objectively determines whether any method that assesses antimicrobial efficacy is reproducible. Because the perception of acceptable reproducibility may differ among stakeholders, the decision process is governed by a stakeholder's specifications that necessarily includes the efficacy of the agents to be tested.

SUBMITTER: Parker AE

PROVIDER: S-EPMC6105646 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Reproducibility of antimicrobial test methods.

Parker Albert E AE Hamilton Martin A MA Goeres Darla M DM

Scientific reports 20180822 1

We review reproducibility results for methods that test antimicrobial efficacy against biofilms, spores and bacteria dried onto a surface. Our review, that included test results for Pseudomonas aeruginosa, Salmonella choleraesuis and Bacillus subtilis, suggests that the level of reproducibility depends on the efficacy of the antimicrobial agent being tested for each microbe and microbial environment. To determine the reproducibility of a method, several laboratories must independently test the s ...[more]

PMID: 30135439

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Project description:Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient???0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet???0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

Project description:MOTIVATION:Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. RESULTS:We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. AVAILABILITY AND IMPLEMENTATION:The RBH construction tool is available from http://goo.gl/DzpwYp. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Dataset Information

Reproducibility of antimicrobial test methods.

Publications

Reproducibility of antimicrobial test methods.

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