Data registration in a first level child growth and development service

Objetivos: Determinar el tiempo empleado en el registro de datos en las consultas de los servicios de crecimiento y desarrollo en un centro de salud. Lugar: Centro de Salud Mi Perú, Dirección de Salud Callao, Perú. Materiales y Métodos: Estudio de tiempos, mediante observación directa, durante el proceso de atención a 51 usuarios en la consulta del Servicio de Crecimiento y Desarrollo. Resultados: Durante la atención a los usuarios del servicio de crecimiento y desarrollo, se usó 5 formatos para el registro de datos. Cada vez que un niño fue atendido, se registró 31 variables; una de ellas fue registrada simultáneamente en 5 formatos, 3 variables en 6 formatos y 7 en 2 formatos. La mediana de tiempo requerido para el registro manual de todos los formatos fue 2,09 minutos, equivalentes a 15,3% del tiempo total de atención a cada niño. Conclusión: El registro de datos en los servicios de crecimiento y desarrollo demanda una alta proporción del tiempo disponible para la atención a los usuarios.Objectives: To determine data registration time in a health center child growth and development service. Setting: Mi Peru Health Center, Callao Health Direction, Peru. Materials and Methods: Study by direct observation of the time used during consultation in 51 users of a growth and development service. Results: Five formats were used for data registration during consultation. Each time a child was attended 31 variables were registered; one variable was simultaneously registered in 5 formats, 3 variables in 6 formats, and 7 in 2 formats. The median time required for handwriting of all formats was 2,09 minutes, equivalent to 15,3% of the overall time used for the child attention. Conclusion: Data registration in the growth and development services demands a considerable proportion of the time available for consultation

SpheraCosmolife: a new tool for the risk assessment of cosmetic products.

A new, freely available software for cosmetic products has been designed that considers the regulatory framework for cosmetics. The software allows an overall toxicological evaluation of cosmetic ingredients without the need for additional testing and, depending on the product type, it applies defined exposure scenarios to derive risk for consumers. It takes regulatory thresholds into account and uses either experimental values, if available, or predictions. Based on the exper­imental or predicted no observed adverse effect level (NOAEL), the software can define a point of departure (POD), which is used to calculate the margin of safety (MoS) of the query chemicals. The software also provides other toxico­logical properties, such as mutagenicity, skin sensitization, and the threshold of toxicological concern (TTC) to provide an overall evaluation of the potential chemical hazard. Predictions are calculated using in silico models implemented within the VEGA software. The full list of ingredients of a cosmetic product can be processed at the same time, at the effective concentrations in the product as given by the user. SpheraCosmolife is designed as a support tool for safety assessors of cosmetic products and can be used to prioritize the cosmetic ingredients or formulations according to their potential risk to consumers. The major novelty of the tool is that it wraps a series of models (some of them new) into a single, user-friendly software system

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

Minimal and Reduced Reversible Automata

International audienceA condition characterizing the class of regular languages which have several nonisomorphic minimal reversible automata is presented. The condition concerns the structure of the minimum automaton accepting the language under consideration. It is also observed that there exist reduced reversible automata which are not minimal, in the sense that all the automata obtained by merging some of their equivalent states are irreversible. Furthermore, it is proved that if the minimum deterministic automaton accepting a reversible language contains a loop in the “irreversible part” then it is always possible to construct infinitely many reduced reversible automata accepting such a language

Registro de datos en un servicio de crecimiento y desarrollo infantil del nivel primario

Objetivos: Determinar el tiempo empleado en el registro de datos en las consultas de los servicios de crecimiento y desarrollo en un centro de salud. Lugar: Centro de Salud Mi Perú, Dirección de Salud Callao, Perú. Materiales y Métodos: Estudio de tiempos, mediante observación directa, durante el proceso de atención a 51 usuarios en la consulta del Servicio de Crecimiento y Desarrollo. Resultados: Durante la atención a los usuarios del servicio de crecimiento y desarrollo, se usó 5 formatos para el registro de datos. Cada vez que un niño fue atendido, se registró 31 variables; una de ellas fue registrada simultáneamente en 5 formatos, 3 variables en 6 formatos y 7 en 2 formatos. La mediana de tiempo requerido para el registro manual de todos los formatos fue 2,09 minutos, equivalentes a 15,3% del tiempo total de atención a cada niño. Conclusión: El registro de datos en los servicios de crecimiento y desarrollo demanda una alta proporción del tiempo disponible para la atención a los usuarios

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

<p>Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligandbased classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.</p&gt