Parker Albert E, Miller Lindsey, Adams Jacob, Pettigrew Charles, Buckingham-Meyer Kelli, Summers Jennifer, Christen Andres, Goeres Darla
Center for Biofilm Engineering, Montana State University, Bozeman, Montana, USA.
Department of Mathematical Sciences, Montana State University, Bozeman, Montana, USA.
J Appl Microbiol. 2022 Dec;133(6):3413-3423. doi: 10.1111/jam.15765. Epub 2022 Sep 13.
To assess removal versus kill efficacies of antimicrobial treatments against thick biofilms with statistical confidence.
A photo-activated chlorine dioxide treatment (Photo ClO ) was tested in two independent experiments against thick (>100 μm) Pseudomonas aeruginosa biofilms. Kill efficacy was assessed by viable plate counts. Removal efficacy was assessed by 3D confocal scanning laser microscope imaging (CSLM). Biovolumes were calculated using an image analysis approach that models the penetration limitation of the laser into thick biofilms using Beer's Law. Error bars are provided that account for the spatial correlation of the biofilm's surface. The responsiveness of the biovolumes and plate counts to the increasing contact time of Photo ClO were quite different, with a massive 7 log reduction in viable cells (95% confidence interval [CI]: 6.2, 7.9) but a more moderate 73% reduction in biovolume (95% CI: [60%, 100%]). Results are leveraged to quantitatively assess candidate CSLM experimental designs of thick biofilms.
Photo ClO kills biofilm bacteria but only partially removes the biofilm from the surface. To maximize statistical confidence in assessing removal, imaging experiments should use fewer pixels in each z-slice, and more importantly, at least two independent experiments even if there is only a single field of view in each experiment.
There is limited penetration depth when collecting 3D confocal images of thick biofilms. Removal can be assessed by optimally fitting Beer's Law to all of the intensities in a 3D image and by accounting for the spatial correlation of the biofilm's surface. For thick biofilms, other image analysis approaches are biased or do not provide error bars. We generate unbiased estimates of removal and assess candidate CSLM experimental designs of thick biofilms with different pixilations, numbers of fields of view and number of experiments using the included design tool.
以统计学置信度评估抗菌治疗对厚生物膜的清除与杀灭效果。
在两项独立实验中，对厚度大于100μm的铜绿假单胞菌生物膜测试了光活化二氧化氯处理（光ClO）。通过活菌平板计数评估杀灭效果。通过三维共聚焦扫描激光显微镜成像（CSLM）评估清除效果。使用一种图像分析方法计算生物体积，该方法利用比尔定律对激光穿透厚生物膜的限制进行建模。提供了误差线，其考虑了生物膜表面的空间相关性。生物体积和活菌计数对光ClO接触时间增加的响应差异很大，活菌数量大幅减少7个对数（95%置信区间[CI]：6.2，7.9），但生物体积减少更为适度，为73%（95%CI：[60%，100%]）。利用这些结果对厚生物膜的候选CSLM实验设计进行定量评估。
光ClO可杀死生物膜细菌，但仅能部分从表面清除生物膜。为了在评估清除时最大化统计学置信度，成像实验在每个z切片中应使用更少的像素，更重要的是，即使每个实验只有一个视野，也应至少进行两项独立实验。
在采集厚生物膜的三维共聚焦图像时，穿透深度有限。可以通过将比尔定律最佳拟合到三维图像中的所有强度并考虑生物膜表面的空间相关性来评估清除情况。对于厚生物膜，其他图像分析方法存在偏差或不提供误差线。我们生成了无偏的清除估计值，并使用所包含的设计工具评估了具有不同像素化、视野数量和实验数量的厚生物膜的候选CSLM实验设计。
