Biology subjects

Patrinos, G. P.

Publications and source records attributed to Patrinos, G. P..

2 recordsLinked to original sources

bioRxiv2023

Introducing multifactorial electroculturomics: Alternating Current Electric Pulses, combined with mild thermal treatment, exhibit antimicrobial or stimulatory effects on bacterial pathogens and enteroviruses, implying prospects for targeted microbiomics applications

AIMSTo surrogate chemical and high-energy microbicidals, Electroceuticals may be used as a stand-alone or combined treatment under the guise of Electroculturomics. METHODS AND RESULTSUsing high and low settings of Intensity and Frequency of a medical-rated instrument (TENS) of alternating current the viability and propagation of seven pathogenic bacteria and one enterovirus of environmental and medical importance were tested in vitro, in order to establish the interaction of electroceuticals and mild pasteurization protocols and identify potential synergies and/or antagonism of these treatments. The combined regimen showed synergy, following the prerogatives of the Bioelectric Effect, and antagonism. High frequency (800Hz) rather than low (2 Hz) seems detrimental, while intensity (10 or 1 mA) seems almost inconsequential, while longer sessions enhance detrimental effects but short exposure may be beneficial. CONCLUSIONSNo single treatment seems optimal for all tested bacteria. High frequency can be effective against low titers of Enterovirus, but at higher titers, the effect may be reversed. Case-specific effects on microbial growth patterns seem to be the norm. SIGNIFICANCE AND IMPACT OF STUDYDiverse mechanisms of microbicidal or stimulatory activity are implied, allowing individualized uses and targeted applications in food and environmental safety, therapeutics and industrial bioprocessing.

microbiology↗

bioRxiv2020

Pixel-based machine learning and image reconstitution for dot-ELISA pathogen serodiagnosis

Serological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB ("Red", "Green", "Blue") pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images which would then be reconstituted by pixels having probabilities above a cutoff that may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

microbiology↗