Biospeckle laser digital image processing for quantitative and statistical evaluation of the activity of ciprofloxacin onEscherichia coliK-12

Abstract

Antibiotic susceptibility testing is a necessary step prior to the treatment of clinical infections. A major concern is the time required to obtain a fast and reliable result. The aim of this work is to use Biospeckle laser in a 15min assay for an antimicrobial susceptibility test of Ciprofloxacin in serial two-fold dilutions on Escherichia coli K-12 using Venereal Disease Research Laboratory (VDRL) plates. Analysis of images by video edition is performed on a quantitatively selected region of interest, and processed with ImageJ-ImageDP; and by the construction of time series and analysis with either statistical diagnostics tests or Autoregressive Integrated Moving Average (ARIMA) models.Antimicrobial susceptibility tests are also performed for the purpose of quantitative comparison, showing a profile that is comparable to the result obtained with ImageJ-ImageDP processing after 15min of antibiotic action. Only the time series of the least affected bacteria (low Ciprofloxacin concentration) behaves in an expected manner, being non-independent and mainly non-linear, non-normal, and heteroscedastic. The most affected bacteria (higher Ciprofloxacin concentration) are non-independent and tend to be linear, normal and heteroscedastic. Adjustment to a linear regression identifies both, the culture medium without bacteria and the most affected bacteria, normality identifies the most affected bacteria and heteroscedasticity-homoscedasticity distinguishes the presenceabsence of bacteria, respectively. ARIMA models (1,1,1)(1,0,1) 11 and (4,1,1)(1,1,1) 11 fit the time series of the most affected bacteria while the latter also fits the culture medium without bacteria. The time series of the least affected bacteria are identified by a (7,1,2)(1,0,1) 11 model. The non-linear, non-normal and heteroscedastic behavior of this group is probably responsible for its adjustment to a model with a relatively high parameter. The four methods: diagnostic statistical tests, fitting of ARIMA models, ImageJ-ImageDP and antimicrobial susceptibility tests, show similar results, being able to distinguish among the groups of assays with bacteria and Ciprofloxacin below and above the Minimal Inhibitory Concentration.

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

“…The potential of laser speckle imaging techniques for antimicrobial susceptibility testing have been earlier demonstrated by many authors (Grassi et al, 2016(Grassi et al, , 2019Zhou et al, 2020). Authors described promising results obtained from variable laser speckle imaging analysis of bacteria growth kinetics in solid and liquid media under influence of different antibiotics.…”

Laser Applications for Estimation of Microbial Activity and Investigation of NIR Skin Autofluorescence

“…Technology using speckle decorrelation time maps has been demonstrated for the detection of E. coli and B. cereus on a meat (chicken breast) [1]. Speckle analysis has also been applied to biomass growth kinetic measurements in liquid culture, characterization of Colony Forming Units (CFU) morphology, determination of antibiotic susceptibility [2][3][4][5]. As well as laser speckle imaging techniques in combination with Deep Learning (DL) and Artificial Neural Networks (ANN) have demonstrated a fast system response of antibacterial susceptibility evaluation in MIC (Minimum Inhibitory Concentration) tests [6].…”

“…where 9:;: [5,6] is the Fourier transform of -<1[=]⋅? [=−5], a complex function representing the phase and magnitude of the signal over time and frequency; 4) marking and determination of the times and spatial locations of detected signals; 5) marking the growth start times of each 20 × 20 pix square, knowing the location of each square in space and its distance from the colony center (marked manually in step 1), finally the colony radius was calculated as a function of time (Fig.…”