Combining food antimicrobials can enhance inhibition of Listeria monocytogenes in ready-to-eat (RTE) meats. A broth dilution assay was used to compare the inhibition of L. monocytogenes resulting from exposure to nisin, acidic calcium sulfate, ε-poly-L-lysine, and lauric arginate ester applied singly and in combination. Minimum inhibitory concentrations (MICs) were the lowest concentrations of single antimicrobials producing inhibition following 24 h incubation at 35 °C. Minimum bactericidal concentrations (MBCs) were the lowest concentrations that decreased populations by ≥3.0 log(10) CFU/mL. Combinations of nisin with acidic calcium sulfate, nisin with lauric arginate ester, and ɛ-poly-L-lysine with acidic calcium sulfate were prepared using a checkerboard assay to determine optimal inhibitory combinations (OICs). Fractional inhibitory concentrations (FICs) were calculated from OICs and were used to create FIC indices (FIC(I)s) and isobolograms to classify combinations as synergistic (FIC(I) < 1.00), additive/indifferent (FIC(I)= 1.00), or antagonistic (FIC(I) > 1.00). MIC values for nisin ranged from 3.13 to 6.25 μg/g with MBC values at 6.25 μg/g for all strains except for Natl. Animal Disease Center (NADC) 2045. MIC values for ε-poly-L-lysine ranged from 6.25 to 12.50 μg/g with MBCs from 12.50 to 25.00 μg/g. Lauric arginate ester at 12.50 μg/g was the MIC and MBC for all strains; 12.50 mL/L was the MIC and MBC for acidic calcium sulfate. Combining nisin with acidic calcium sulfate synergistically inhibited L. monocytogenes; nisin with lauric arginate ester produced additive-type inhibition, while ε-poly-L-lysine with acidic calcium sulfate produced antagonistic-type inhibition. Applying nisin along with acidic calcium sulfate should be further investigated for efficacy on RTE meat surfaces.

It has been hypothesized that inhibition of foodborne pathogens can be enhanced by using antimicrobials in combination. A broth dilution assay was devised to determine whether inhibition of Listeria monocytogenes exposed to the combination of the fatty acid octanoic acid (OCT) and the organic acid-containing antimicrobial acidic calcium sulfate (ACS) was enhanced compared with the inhibition of the pathogen exposed to either antimicrobial applied singly. MICs for OCT and ACS were 25.00 μg/g and 1.56 ml/liter, respectively, for all strains of the pathogen tested. Fractional inhibitory concentrations (FICs) from the combination exposures were calculated for use in characterizing the antimicrobial interaction as antagonistic, additive indifferent, or synergistic with respect to L. monocytogenes inhibition. Combining OCT and ACS resulted in observed synergistic inhibition of L. monocytogenes; isobolograms for all strains curved toward the origin, and FIC indices (FIC(I)s) were <1.0. Future investigations of the antimicrobial combination should focus on determining the mechanism of action of combined antimicrobials and the levels of antimicrobials required for pathogen inhibition on the surfaces of ready-to-eat meats.

No abstract

Background Multiple Sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging, and multimodal biomarkers to define the risk of disease activity. Methods We have analyzed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centers, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Validation was conducted in an independent prospective cohort of 271 MS patients from a single center. Results We found algorithms for predicting confirmed disability accumulation for the different scales, No Evidence of Disease Activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors by using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in the discovery and validation cohorts. Conclusion Combining clinical, imaging, and omics data with machine learning helps to identify MS patients at risk of disability worsening.

The author, studying a fresh fetus born of a typhoid mother, wants to confirm Eberth's view that microorganisms that enter the blood of fetuses born from typhoid mothers are indeed the real typhoid bacillus, and not some or any other saprophyte.