One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.
Introduction: Contributions to medicine may come from different areas; and most areas are full of researchers wanting to support. Physists may help with theory, such as for nuclear medicine. Engineers with machineries, such as dialysis machine. Mathematicians with models, such as pharmacokinetics. And computer scientists with codes such as bioinformatics. Method: We have used TensorFlow.js for modeling using neural networks biomedical datasets from Kaggle. We have modeled three datasets: diabetes detection, surgery complications, and heart failure. We have used Angular coded in TypeScript for the implementation of the models. Using TensorFlow.js, we have built Multilayer Perceptrons (MPLs) for modelling our datasets. We have employed the training and the validation curves to make sure the model learnt, and we have used accuracy as a measure of goodness of each model. Results and discussion: We have built a couple of examples using TensorFlow.js as machine learning platform. Even though python and R are dominant at the moment, JavaScript and derivatives are growing fast, offering basically the same performance, and some extra features associated with JavaScript. Kaggle, the public platform from where we downloaded our datasets, offers a huge amount of datasets for biomedical cases, thus, the reader can easily test what we have discussed, using the same codes, with minor chances, on any case they may be interested in. We were able to find 92% of accuracy for diabetes detection, 100% for surgery complications, and 70% for heart failure. The possibilities are unlimited, and we believe that it is a nice option for researchers aiming at web applications, especially, focused on medicine.
Atualmente, a taxa de empreendedorismo da população adulta brasileira é 38%, realidade que pode propiciar a criação de startups. Não existe uma definição única para startup, mas a visão mais aceita a define como um ‘modelo de negócio’ interino, cujo objetivo é alcançar um modelo de negócio escalável e repetitivo. O objetivo principal deste artigo é abordar as startups de forma introdutória, adotando como foco o Brasil e a área de saúde. Apesar das peculiaridades do Brasil, dentre as quais o fato de que grande parte da pesquisa é financiada pelo governo, o País é bastante ‘globalizado’, o que torna teorias desenvolvidas em outros países válidas para nossa realidade. Tal observação, entre outros fatores, motivou a escrita do artigo baseado quase exclusivamente em materiais escritos na língua inglesa. Conclui-se, como resultado das leituras, que startup é um conceito presente no Brasil e, adicionalmente, que startups na área de saúde precisam de atenção especial.
The ambition of this document is to set in evidence the prerequisite for integrative (mathematical) models, mechanism-based models, for appetite/bodyweight control. For achieving this goal, it is provided a scrutinized literature review and it is organized them in such a way to make the point. The quantitative methods exploited by the authors are called differential equations solved numerically; they are discussed briefly since it is not our goal herein to handle details. On the current state of the art, there is no mathematical model to the best of the author’s knowledge targeting at integrating several hormones at once in mathematical descriptions: even for single hormones, the literature is either occasional or do not exist at all; it is depicted some results for simple models already built. As it can be seen, the functions and roles seem fuzzy, most hormones seem to be piloting the same undertaking. The key challenge from a mathematical modeling perspective is how to separate properly the mechanisms of each hormone. The kind of pursuit presented herein could initiate an imperative cascade of mathematical modeling applied to metabolism of bodyweight control and energy homeostasis.
Nos últimos anos, tem havido um crescimento, quase exponencial, dos custos com saúde. Como consequência, formas diversas têm sido propostas e implementadas para lidar com as demandas na área de saúde. Nos tempos atuais, estas demandas envolvem tratamentos cada vez mais eficientes, personalizados, acessíveis e de baixo custo. Diante deste cenário, pode-se dizer que as chamadas startups são a grande promessa para lidar com iniciativas, como implementação de novas ideias e produtos. As startups trazem novas formas de pensar e produzir, com um grau de liberdade não presente em grandes empresas. Pretende-se discutir espaços para startups na área de saúde, em diálogo com literaturas disponíveis na internet. O objetivo deste trabalho é evidenciar como as startups podem contribuir para a medicina personalizada alcançar seus objetivos, healthcare, em geral, superando o dilema da ‘personalização vs. custo’. A motivação é que apesar do desenvolvido das universidades, muitos países, sendo o Brasil um desses, não possuem um fluxo contínuo e corriqueiro de transformação de ciência em bens para a sociedade. Conclui-se que as startups se apresentam como a melhor opção para resolver/lidar com vários problemas/dilemas que surgiram nas áreas médicas, nas últimas décadas, como aumento dos custos e demanda cada vez maior por tratamentos especializados/personalidades.
Food intake, bodyweight and appetite are controlled by a "web of hormones." Recently, from this web of hormones, several hormones have been revealed and investigated with different degrees of success: a key one is ghrelin. Accordingly, ghrelin is an orexigenic (i.e., appetite stimulant) hormone; in fact, the only one of its kind, a peripheral hormone that can influence, centrally, one's propensity to start a meal. On this work, we shall present a problem in parameter estimation using evolutionary algorithms in conjunction with local search, what we have called herein hybrid algorithms (global search + local search); additionally, we apply artificial neural networks (feedforward neural network) for supporting the numerical simulations (what we have named "fake data"). Moreover, we present a mathematical model for ghrelin partially published elsewhere by the same authors; in addition, we have confronted the model mathematically with in vivo data via parameter estimation and got promising results for the novel mathematical formulation for ghrelin dynamics. Thus, our aim is showing that our algorithms can be imperative for fitting the current and future versions of the model. Notwithstanding the parameter estimation was unable to model precisely the experimental data, most likely due to physiological details still unclear in the medical literature, it generated an optimized curve relatively close to the experimental data, leaving promising results for future investigations.
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