BackgroundsShigellosis remains an important public health problem in developing countries with S. sonnei and S. flexneri in US, Europe and in Asian countries being of importance.ObjectivesThis study evaluates the protective effect of Lactobacillus casei cell-free culture supernatants (CFCS) against multiple drug resistance (MDR) clinical samples of Shigella sonnei and Shigella flexneri in vitro.Materials and MethodsS. sonnei and S .flexneri was identified by common microbiological and serological methods. Antibiogram with 18 antibiotics were tested for 34 positive cultures by disc diffusion method. The Samples showed considerable resistance to antibiotics. Antimicrobial effects of CFCS were tested against S. sonnei and S. flexneri by agar-well assay and broth micro dilution methods. In addition, the antimicrobial activity remained active treatment after adjust pH 7, adding Proteinase K and heating for L. casei.ResultsThe results implicate that L. casei strongly inhibits the development of pathogen samples. In contrast, via the disc diffusion method 4 out of 18 antibiogram have shown complete resistance against the pathogen samples. In addition, the natures of antimicrobial properties have been tested in different conditions such as various pH, temperature and presence of proteinase K. The MIC50 (minimum inhibitory concentration) and MIC90 of CFCS of L. casei were determined, for S. sonnei were 2.25 and 10.5, for S .flexneri were 5.25 and 5.25 respectively. The results have shown a significant resistance pattern by these four antibiotics in this case.ConclusionsThe data indicates that. L. casei highly resistant against to antibiotics, heat, Proteinase K and so many activities against MDR Shigella pathogenic strains . L. casei is the best probiotics candidate.
Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any “extensions library” requirement from Agents.jl, which is paramount in many other tools.
The dynamics of populations evolving on an adaptive landscape depends on multiple factors, including the structure of the landscape, the rate of mutations, and effective population size. Existing theoretical work often makes ad hoc and simplifying assumptions about landscape structure, whereas experimental work can vary important parameters only to a limited extent. We here overcome some of these limitations by simulating the adaptive evolution of RNA molecules, whose fitness is determined by the thermodynamics of RNA secondary structure folding. We study the influence of mutation rates and population sizes on final mean population fitness, on the substitution rates of mutations, and on population diversity. We show that evolutionary dynamics cannot be understood as a function of mutation rate µ, population size N, or population mutation rate Nµ alone. For example, at a given mutation rate, clonal interference prevents the fixation of beneficial mutations as population size increases, but larger populations still arrive at a higher mean fitness. In addition, at the highest population mutation rates we study, mean final fitness increases with population size, because small populations are driven to low fitness by the relatively higher incidence of mutations they experience. Our observations show that mutation rate and population size can interact in complex ways to influence the adaptive dynamics of a population on a biophysically motivated fitness landscape.
Agent-based modeling involves designing a system of autonomous agents that interact based on a set of given rules (Grimm et al., 2006). It is used for studying complex systems whose behavior cannot be easily identified using classical mathematical approaches.
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