Antimicrobial peptides of the vaginal innate immunity and their role in the fight against sexually transmitted diseases

Some antimicrobial peptides (AMPs) are produced in the vaginal innate immune system and play an important role in protecting this organ against pathogenic agents. Moreover, sexually transmitted diseases have become a major problem in human societies and are rapidly spreading. The emergence of antibiotic-resistant microbes (superbugs) can pose a major threat to human societies and cause rapid spread of these diseases. Finding new antimicrobial compounds to fight superbugs is therefore essential. It has been shown that AMPs have good potential to become new antibiotics. The most important AMPs in the vaginal innate immune system are defensins, secretory leucocyte protease inhibitors, calprotectin, lysozyme, lactoferrin and elafin, which play an important role in host defence against sexually transmitted infections, modulation of immune responses and anticancer activities. Some AMPs, such as LL-37, magainin 2 and nisin, show both spermicidal and antimicrobial effects in the vagina. In this summary, we will discuss vaginal AMPs and continue to address some of the challenges of using peptides to control pathogens that are effective in sexually transmitted diseases. © 2019 The Author(s

B Cell Epitopes of Four Fimbriae Antigens of Klebsiella pneumoniae: A Comprehensive In Silico Study for Vaccine Development

Klebsiella pneumoniae is one of the major causes of nosocomial infections worldwide which can cause several diseases in children and adults. The globally dissemination of hyper-virulent strains of K. pneumoniae and the emergence of antibiotics-resistant isolates of this pathogen narrows down the treatment options and has renewed interest in its vaccines. Vaccine candidates of Klebsiella pneumoniae have not been adequately protective, safe and globally available yet. In K. pneumoniae infection, it is well known that B cells that induce robust humoral immunity are necessary for the host complete protection. Identifying the B cell epitopes of antigens is valuable to design novel vaccine candidates. In the present study using immunoinformatics approaches we found B cell epitopes of four K. pneumoniae type 1 fimbriae antigens namely FimA, FimF, FimG, and FimH. Linear and conformational B cell epitopes of each antigen were predicted using different programs. Subsequently, many bioinformatics assays were applied to choose the best epitopes including prediction antigenicity, toxicity, human similarity and investigation on experimental records. These assays resulted in final four epitopes (each for one Fim protein). These final epitopes were modeled and their physiochemical properties were estimated to be used as potential vaccine candidates. Altogether, we found four B cell epitopes of K. pneumoniae Fim antigens that are immunogen, antigenic, not similar to human peptides, not allergen and not toxic. Also, they have suitable physiochemical properties to administrate as vaccine, although their complete efficacy should be also shown in vitro and in vivo. © 2020, Springer Nature B.V

Non-linear Autoregressive Neural Networks to Forecast Short-Term Solar Radiation for Photovoltaic Energy Predictions

Nowadays, green energy is considered as a viable solution to hinder CO2 emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover 40% of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, solar energy will play a crucial role. However, reliable models and tools are needed to forecast and estimate with a good accuracy the renewable energy production in short-term time periods. These tools will unlock new services for smart grid management. In this paper, we propose an innovative methodology for implementing two different non-linear autoregressive neural networks to forecast Global Horizontal Solar Irradiance (GHI) in short-term time periods (i.e. from future 15 to 120min). Both neural networks have been implemented, trained and validated exploiting a dataset consisting of four years of solar radiation values collected by a real weather station. We also present the experimental results discussing and comparing the accuracy of both neural networks. Then, the resulting GHI forecast is given as input to a Photovoltaic simulator to predict energy production in short-term time periods. Finally, we present the results of this Photovoltaic energy estimation discussing also their accuracy