Volumen 57 (2018): Heft 3 (January 2018)

The constantly growing number of multidrug-resistant bacterial strains prompts the search for alternative treatments. Synthetic peptides based on natural antimicrobial peptides, also known as antimicrobial lipopeptides, can become a promising group of “drugs” to fight multi-resistant bacteria. The present paper discusses the origins of synthetic lipopeptides, their classification and antimicrobial properties.

1. Introduction. 2. Antimicrobial peptides. 3. Classification of antimicrobial peptides. 4. Lipopeptide antibiotics. 5. Synthetic lipopeptides. 5.1. Ultrashort lipopeptides. 5.2. Peptidomimetics. 5.3. Multivalent lipopeptides. 5.4. Hydrocarbon-stapled lipopeptides. 5.5. Antimicrobial lipopeptides in laboratory researches. 6. Summary

The beginnings of predictive microbiology date back to 1920 when Bigelow developed a logarithmic-linear dependence of kinetics on the death of microorganisms. Predictive microbiology is a sub-discipline of food microbiology, whose task is to predict the behavior of microorganisms in food using mathematical models. The predictive model for microbiology is usually a simplified description of the correlation between the observed reactions and the factors responsible for the occurrence of these reactions. There are several main conceptual models (empirical vs. mechanistic, stochastic vs. deterministic, dynamic vs. static), in which there are model divisions depending on the type of examined microorganism or the nature of the problems caused by microbes (kinetic vs. probabilistic), described variables (first, secondary and tertiary) or the influence of environmental factors on microbial populations (growth, survival, inactivation). The new generations of models include molecular and genomic models, transfer models, Artificial Neural Network, interactions between species, and single cell models.

The process of creating a mathematical model requires coordination of work and the knowledge of: microbiology, statistics, mathematics, chemistry, process engineering and computer and web science. It also requires appropriate hardware and software. There are four stages in the construction of a mathematical model: planning; data collection and analysis; mathematical description; validation and storage of data.

In recent years, numerous computer software programs have been developed: FISHMAP, FSSP, Dairy Product Safety Predictor, Symbiosis, GroPIN, Listeria Meat FDA-iRISK, TRiMiCri, Microbial Responses, GlnaFiT, FILTREX, PMM-Lab. ComBase database, on the other hand, is a pioneering achievement as an on-line tool. Some programs meet the requirements for creating Food Safety Model Repositories (FSMR).

1. Introduction. 2. The idea of predictive microbiology. 3. Historical background of predictive microbiology. 4. The concept of a model and modeling concepts in food microbiology. 4.1. Concept 1: empirical vs. mechanistic models. 4.2. Concept 2: static vs. dynamic models. 4.3. Concept 3: stochastic vs. deterministic models. 5. Breakdowns of prognostic models. 5.1. Neural networks. 5.2. A new generation of predictive models. 6. The construction of the predictive model. 6.1. Planning the experiment. 6.2. Collection of data. 6.3. Data analysis. 6.4. Model validation. 7. Predictive microbiology in risk analysis. 8. Summary

Drug-resistant bacteria from the genus Enterococcus are currently among the most important pathogens behind healthcare-associated infections. The drug resistance of these bacteria has been on the increase since the 1980s, leeding to their multi-drug resistance. Selective pressure, present mainly in the hospital environment, contributed to this phenomenon. However, also outside the hospital environment selective pressure comes into play, namely the use of antibiotics as promoters of growth in animal husbandry and in food production. Household pets form a reservoir of drug-resistant enterococcal strains, too. The exchange of resistance genes between enterococcal strains from different niches poses a threat to public health.

1. Introduction. 2. Hospital environment. 3. Farm animals. 4. Food. 5. Household pets. 6. Summary

The studies on the occurrence and diversity of tick-borne infections in HIV-infected individuals have been few, and the subject has been relatively neglected when compared with other infections associated with HIV. Non-specific symptoms of tick-borne diseases pose a challenge in clinical care and may lead to misdiagnosis, especially in HIV-positive patients, who often experience many non-specific clinical symptoms. Additionally, in immunocompromised patients, a significant delay of antibody production may occur, and the results of a serological test may be misinterpreted. This review focuses on the most common tick-borne infections in HIV-positive patients in Europe.

1. Introduction. 2. Ticks as vectors. 3. Babesiosis. 3.1. Diagnostics and treatment. 4. Lyme borreliosis. 4.1. Diagnostics and treatment. 5. Rickettsiosis. 5.1. Diagnostics and treatment. 6. Conclusions

Campylobacter spp. are Gram-negative, spiral, thermophilic, motile bacteria, which require microaerophilic environment for growth. They have restricted carbohydrate catabolism, but have well-developed mechanism of acquiring micronutrients instead. A common problem, especially in developing countries, is campylobacteriosis, mostly caused by Campylobacter jejuni. The major reason of this disease is the increasing resistance of these bacteria to commonly used antibiotics. The most frequent source of infection is poorly cooked poultry meat. Despite numerous cases of campylobacteriosis, its pathogenesis is not fully understood. However, the role of bacterial motility, adhesion, ability to invade hosts intestinal epithelial cells and secretion of toxins have been found significant. In addition to developing gastrointestinal infections, C. jejuni is firmly established as a causative agent of Guillain-Barré Syndrome, which is an autoimmune-mediated demyelinating polyneuropathy of peripheral nerves. Molecular mimicry between bacterial surface structures and hosts gangliosides is responsible for the development of this disease. The serious local and systemic consequences of C. jejuni infections are the reason for monitoring the microbial purity of food, especially meat and drinking water, for C. jejuni contamination necessitating also new approaches to contamination prevention or minimization.

1. Introduction. 2. Colonization and transmission pathways for Campylobacter spp. 3. The pathogenesis of Campylobacter spp. 3.1. Virulence factors. 4. Systemic consequences of Campylobacter spp. infections in humans. 4.1. Role of C. jejuni infection in demyelination of peripheral nerves. 4.2. Antigenic mimicry between host gangliozydes and C. jejuni. 4.3. Role of cytokines in the development of GBS. 4.4. The strategy in Guillain-Barré Syndrome therapy. 5. Summary

Staphylococci belong to bacteria often isolated from clinical material obtained from animals. Unlike in human medicine, in veterinary, different species of coagulase-positive staphylococci are isolated from clinical specimens, and except Staphylococcus aureus, Staphylococcus pseudintermedius, and other species are also often recognized. Recently, the taxonomy of staphylococci has been updated, therefore, now it is necessary to recognize the new species as well. Currently, coagulase-negative staphylococci are considered an important group of opportunistic pathogens. The accurate identification of species within the genus Staphylococcus is important because, according to the EUCAST and CLSI recommendations, the interpretation of the results of susceptibility testing for S. aureus and coagulase-negative staphylococci is different. Furthermore, the resistance to methicillin in S. aureus strains is detected using a cefoxitin disk, whereas in the case of S. pseudintermedius– using an oxacillin disk. An important problem for veterinary microbiological laboratories is a limited number of unified guidelines on methodology and guidelines specifying the interpretation of the results of antibiotic susceptibility testing. The lack of available recommendations for some antibiotics testing results in the fact that veterinary laboratories often use the guidelines established for human pathogens. There is an urgent necessity for harmonization of methods and to develop guidelines for the interpretation of results of susceptibility testing for different bacteria, including various species of staphylococci from the individual animal host.

1. Introduction. 2. Problems with the identification of staphylococci isolated from animals. 3. Determination of susceptibility of staphylococci – traditional methods. 4. Alternative methods for determining the susceptibility of staphylococci. 5. Detection of staphylococcal resistance to methicillin. 6. Interpretation of the results of the susceptibility testing of veterinary pathogens. 7. Prevention of the antimicrobial resistance. 8. Summary

Modern technologies of bioethanol production require distillery yeast characterized by thermotolerance, osmotolerance and increased resistance to secondary metabolites. To date, no strains have been observed in nature which possess all of the above-mentioned characteristics. For many years, intensive research has been carried out to improve the technological properties of industrial strains. A number of methods have been developed to allow genetic improvement of distillery yeasts. One of the most promising and effective methods is genome shuffling, allowing the creation of hybrids whose genome is a combination of large DNA fragments derived from strains with distinct phenotypic traits. Genome shuffling creates a chance that the new strain will have valuable functional genes, including their full operons. This, in turn, increases the chance of a long-term maintenance of beneficial technological features by the obtained hybrids.

1. Introduction. 2. Yeast Saccharomyces cerevisiae. 2.1. Yeast genome. 2.2. Role of Saccharomyces cerevisiae yeast in the bioethanol production. 3. Pathways of genetic improvement. 4. Methods of genetic improvement. 5. Genome shuffling. 5.1. Improvement of Saccharomyces cerevisiae yeast strains by genome shuffling method. 6. Conclusion