Application of artificial intelligence to the in silico assessment of antimicrobial resistance and risks to human and animal health presented by priority enteric bacterial pathogens.

Each year, approximately one in eight Canadians are affected by foodborne illness, either through outbreaks or sporadic illness, with animals being the major reservoir for the pathogens. Whole genome sequence analyses are now routinely implemented by public and animal health laboratories to define epidemiological disease clusters and to identify potential sources of infection. Similarly, a number of bioinformatics tools can be used to identify virulence and antimicrobial resistance (AMR) determinants in the genomes of pathogenic strains. Many important clinical and phenotypic characteristics of these pathogens can now be predicted using machine learning algorithms applied to whole genome sequence data. In this overview, we compare the ability of support vector machines, gradient-boosted decision trees and artificial neural networks to predict the levels of AMR within Salmonella enterica and extended-spectrum ß-lactamase (ESBL) producing Escherichia coli. We show that minimum inhibitory concentrations (MIC) for each of 13 antimicrobials for S. enterica strains can be accurately determined, and that ESBL-producing E. coli strains can be accurately classified as susceptible, intermediate or resistant for each of seven antimicrobials. In addition to AMR and bacterial populations of greatest risk to human health, artificial intelligence algorithms hold promise as tools to predict other clinically and epidemiologically important phenotypes of enteric pathogens.

The effects of diltiazem on growth, reproduction, energy reserves, and calcium-dependent physiology in Daphnia magna.

With the growth of both the pharmaceutical industry and the human population and longevity, more drugs are used and processed each day. Inevitably, these pharmaceuticals enter wastewater through human excretion and improper disposal of leftovers. One such medication, diltiazem, a calcium channel blocker, is of importance due to its widespread consumption, and prevalence in aquatic environments. To study the sub-lethal effects of diltiazem on aquatic animals, we investigated its impacts no feeding behaviour, heart rate, respiration, growth, and reproduction of a bioindicator species, Daphnia magna. When exposed to environmentally relevant concentrations, D. magna increased their heart rate by 12% and oxygen consumption by 48%. However, exposure did not have any effects on thoracic limb movement frequency or peristalsis (i.e. feeding behaviour). Individuals exposed to diltiazem for a longer duration (16 days) showed a 44% decrease in lipid reserves and produced between 17 and 28% fewer neonates which were 10-12% larger. Our study demonstrated that exposure to diltiazem creates an energy imbalance in D. magna which could, in the long run, influence their populations.