Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.
Epilepsy is characterized by the recurrence of epileptic seizures that affect secondary physiological changes in the patient. This leads to a series of adverse events in the manifestation of convulsions in an uncontrolled environment and without medical help, resulting in risk to the patient, especially in people with refractory epilepsy where modern pharmacology is not able to control seizures. The traditional methods of detection based on wired hospital monitoring systems are not suitable for the detection of long-term monitoring in outdoors. For these reasons, this paper proposes a system that can detect generalized tonic-clonic seizures on patients to alert family members or medical personnel for prompt assistance, based on a wearable device (glove), a mobile application and a Support Vector Machine classifier deployed in a system based on cloud computing. In the proposed approach we use Accelerometry (ACC), Electromyography (ECG) as measurement signals for the development of the glove, a machine learning algorithm (SVM) is used to discriminate between simulated tonic-clonic seizures and non-seizure activities that may be confused with convulsions. In this paper, the high level architecture of the system and its implementation based on Cloud Computing are described. Considering the traditional methods of measurement, the detection system proposed in this paper could mean an alternative solution that allows a prompt response and assistance that could be lifesaving in many situations.
--This paper presents the results of a survey about technovigilance carried out in 21 clinical institutions in southwest Colombia. It also provides an analysis of how these programs take into account different risk management methodologies in order to create awareness of the importance of patient safety in all staff members and improve quality of the health services provided.Keywords --Technovigilance, vigilance, risk management, patient safety, medical devices.
La TecnovigiLancia y La gesTión de Riesgos como heRRamienTas paRa mejoRaR La seguRidad de Los pacienTes en Las insTiTuciones de saLud coLombianasResumen--Este trabajo presenta los resultados de una encuesta acerca de la tecnovigilancia realizada en 21 instituciones de salud del suroeste de Colombia. Adicionalmente proporciona un análisis de cómo estos programas consideran diferentes metodologías de manejo de riesgos para crear conciencia en todos los empleados de la importancia de la seguridad de los pacientes y así mejorar la calidad de los servicios de salud prestados. Resumo--Este trabalho apresenta os resultados de uma pesquisa a respeito da vigilância tecnológica levada a cabo em 21 instituições de saúde do sudoeste da Colômbia. Adicionalmente proporciona uma análise de como estes programas consideram diferentes metodologias do controle de riscos para criar consciência em todos os empregados da importância da segurança dos pacientes e assim melhorar a qualidade dos serviços de saúde emprestados.
Palabras clave --Palavras-chave --Tecnovigilância, vigilância, controle do risco, saúde do paciente, dispositivos médicos
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