Effect of Paternal Diet on Spermatogenesis and Offspring Health: Focus on Epigenetics and Interventions with Food Bioactive Compounds

Infertility is a growing public health problem. Consumption of antioxidant bioactive food compounds (BFCs) that include micronutrients and non-nutrients has been highlighted as a potential strategy to protect against oxidative and inflammatory damage in the male reproductive system induced by obesity, alcohol, and toxicants and, thus, improve spermatogenesis and the fertility parameters. Paternal consumption of such dietary compounds could not only benefit the fathers but their offspring as well. Studies in the new field of paternal origins of health and disease show that paternal malnutrition can alter sperm epigenome, and this can alter fetal development and program an increased risk of metabolic diseases and breast cancer in adulthood. BFCs, such as ascorbic acid, α-tocopherol, polyunsaturated fatty acids, trace elements, carnitines, N-acetylcysteine, and coenzyme Q10, have been shown to improve male gametogenesis, modulate epigenetics of germ cells, and the epigenetic signature of the offspring, restoring offspring metabolic health induced by stressors during early life. This indicates that, from a father’s perspective, preconception is a valuable window of opportunity to start potential nutritional interventions with these BFCs to maximize sperm epigenetic integrity and promote adequate fetal growth and development, thus preventing chronic disease in adulthood.     

Identification of RAD51–BRCA2 Inhibitors Using N-Acylhydrazone-Based Dynamic Combinatorial Chemistry

RAD51 is an ATP-dependent recombinase, recruited by BRCA2 to mediate DNA double-strand breaks repair through homologous recombination and represents an attractive cancer drug target. Herein, we applied for the first-time protein-templated dynamic combinatorial chemistry on RAD51 as a hit identification strategy. Upon design of N-acylhydrazone-based dynamic combinatorial libraries, RAD51 showed a clear templating effect, amplifying 19 N-acylhydrazones. Screening against the RAD51–BRCA2 protein–protein interaction via ELISA assay afforded 10 inhibitors in the micromolar range. Further ¹⁹F NMR experiments revealed that 7 could bind RAD51 and be displaced by BRC4, suggesting an interaction in the same binding pocket of BRCA2. These results proved not only that ptDCC could be successfully applied on full-length oligomeric RAD51, but also that it could address the need of alternative strategies toward the identification of small-molecule PPI inhibitors.     

Histopathological changes in gill, liver and kidney of neotropical fish Colossoma macropomum exposed to paraquat at different temperatures

This work focused on the histological alterations in gill, liver and kidney of fish Colossoma macropomum exposed to different temperatures (18°C, 29°C, 35°C) with 10mg/L of herbicide Paraquat (PQ), during 21 days. The fish exhibited histopathological changes in these tissues; the most important alteration in gills was telangiectasis. Liver showed debris accumulation inside cytoplasm hepatocytes, karyolysis, karyohesis and a decrease in the size of sinusoids. Hyperplasia of melanomacrophagic centers (MMC) and an increase in basophils were observed in kidney. The lesion inducing by PQ and the damage in tissue depended of temperature exposure fish. The severity of lesions clearly differed among organs with the liver showing the most extensive damages followed in order by the kidney and gills. In PQ/18°C group it was observed the changes in the pattern of lesions, with kidney showing higher damage followed gills and liver.     

A Feasibility Study of High-Entropy Alloy Coating Deposition by Detonation Spraying Combined with Laser Melting

In this work, a new two-stage approach to the deposition of high-entropy alloy coatings is proposed. At the first stage, a composite precursor coating is formed by detonation spraying of the metal powder mixtures. At the second stage, the precursor coating is re-melted by a laser, and the formation of multi-component solid solution phases can be expected upon solidification. The feasibility of the proposed approach was validated using three different mixtures of Fe, Ni, Cu, Co and Al powders. It was shown that detonation spraying allows forming composite coatings with a uniform distribution of the lamellae of different metals. The results of the structural analysis of the laser-treated coatings suggest that complete alloying occurred in the melt and face-centered cubic solid solutions formed in the coatings upon cooling.     

Modeling the COVID-19 epidemic in Croatia: a comparison of three analytic approaches

AimTo facilitate the development of a COVID-19 predictive model in Croatia by analyzing three different methodological approaches.MethodWe used the historical data to explore the fit of the extended SEIRD compartmental model, the Heidler function, an exponential approximation in analyzing electromagnetic phenomena related to lightning strikes, and the Holt-Winters smoothing (HWS) for short-term epidemic predictions. We also compared various methods for the estimation of R0.ResultsThe R0 estimates for Croatia varied from 2.09 (95% CI 1.77-2.40) obtained by using an empirical post-hoc method to 2.28 (95% CI 2.27-2.28) when we assumed an exponential outbreak at the very beginning of the COVID-19 epidemic in Croatia. Although the SEIRD model provided a good fit for the early epidemic stages, it was outperformed by the Heidler function fit. HWS achieved accurate short-term predictions and depended the least on model entry parameters. Neither model performed well across the entire observed period, which was characterized by multiple wave-form events, influenced by the re-opening for the tourist season during the summer, mandatory masks use in closed spaces, and numerous measures introduced in retail stores and public places. However, an extension of the Heidler function achieved the best overall fit.ConclusionsPredicting future epidemic events remains difficult because modeling relies on the accuracy of the information on population structure and micro-environmental exposures, constant changes of the input parameters, varying societal adherence to anti-epidemic measures, and changes in the biological interactions of the virus and hosts.

Epidemiological modeling is one of the main tools in infectious disease epidemic management. The recent COVID-19 pandemic is no exception, despite the prevalent neglect of evidence-based medicine principles (1,2). One of the most commonly used tools for epidemiological modeling are compartmental models, which assume that the dynamics of an epidemic depends on several discrete states, including susceptible, infected, and recovered status (3). The main advantage of these models includes the possibility of predicting the overall epidemic pattern, and their main limitation is heavy dependence on the input parameters (4,5). Numerous other approaches have been used for this purpose, relying on exploring historical patterns in predicting future events (6). The main disadvantage of such models is dependence on the initial parameters and the inability to capture the timely and relevant information in the population, which raises the need for reliable predictive tools that depend less on the starting assumptions.Therefore, we aimed to compare various methodological approaches to epidemic prediction, with a particular focus on the performance of methods that do not require numerous inputs and rely less on the initial assumptions.