Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models.

Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets.

Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology.

First-principles-based kinetic Monte Carlo simulations of CO oxidation on catalytic Au(110) and Ag(110) surfaces.

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms.

From biomedicinal to models and back to therapeutics: a review on the advancement of peptidic modeling.

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an model versus an experimental validation scenario, this review aims to summarize and discuss how different techniques contribute at present to the design of peptide-based molecules.

QSAR modelling: a therapeutic patent review 2010-present.

Introduction: Quantitative Structure-Activity Relationship (QSAR) models are becoming one of the most interesting fields for developing therapeutics and therapeutics related patents. At present, QSAR methodologies comprise a series of possibilities, including joining forces with machine learning methods and increasing even more the swiftness they might bring to the prospective development of therapeutics in the Health Sciences scope.

Areas Covered: After evaluating the period from 2010 to early 2018, the areas covered by the reviewed QSAR based therapeutics patents comprise three main fields (drug development, risk assessment and novel QSAR methodologies), and several areas, from cancer and cancer related symptomatology to neurodegenerative diseases, such as Parkinson's disease, or even monitoring several chemical particles carrier-mediums or interface frontiers.

Lifetime reproductive efficiency of BALB/c mouse pairs after an environmental modification at 3 mating ages.

The aim of this study was to evaluate the effects of an environment change and the age at which mating pairs were formed on the lifetime reproductive performance of BALB/c mice. We assigned 60 monogamous pairs to a randomized design in a 2 × 3 factorial arrangement (with or without an environmental modification and with 3 mating ages: 28, 45, or 60 d). Autoclaved cardboard tubes (length, 10 cm; diameter, 4 cm) were used as the environmental modification.

Association of IGF1 and KDM5A polymorphisms with performance, fatness and carcass traits in chickens.

Two functional and positional candidate genes were selected in a region of chicken chromosome 1 (GGA1), based on their biological roles, and also where several quantitative trait loci (QTL) have been mapped and associated with performance, fatness and carcass traits in chickens. The insulin-like growth factor 1 (IGF1) gene has been associated with several physiological functions related to growth. The lysine (K)-specific demethylase 5A (KDM5A) gene participates in the epigenetic regulation of genes involved with the cell cycle.

[The practice of nursing auxiliaries in the family health program].

This study aims to evaluate the practice of nursing auxiliaries in the Family Health Program, identifying their daily activities, as well as factors that facilitate or hinder their work. A qualitative approach was chosen, using the focal group technique, with the participation of 34 nursing auxiliaries from the Federal District, Brazil. It was found that the nursing auxiliaries carry out a variety of activities, both of individual and collective nature, heavily influenced by the clinical model and with different degrees of complexity, such as: family registration, brief clinical evaluation, group activities, technical procedures, home visits, sanitary and epidemiological vigilance in day care centers.