Calle-Arroyo, C. (Carlos) de la

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    Optimal designs for Antoine's Equation: compound criteria and multi-objective designs via genetic algorithms
    (2023) Calle-Arroyo, C. (Carlos) de la; Rodríguez-Aragón, L.J. (Licesio J.); González-Fernández, M.A. (Miguel Ángel)
    Antoine's Equation is commonly used to explain the relationship between vapour pressure and temperature for substances of industrial interest. This paper sets out a combined strategy to obtain optimal designs for the Antoine Equation for D- and I-optimisation criteria and different variance structures for the response. Optimal designs strongly depend not only on the criterion but also on the response's variance, and their efficiency can be strongly affected by a lack of foresight in this selection. Our approach determines compound and multi-objective designs for both criteria and variance structures using a genetic algorithm. This strategy provides a backup for the experimenter providing high efficiencies under both assumptions and for both criteria. One of the conclusions of this work is that the differences produced by using the compound design strategy versus the multi-objective one are very small.
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    Protein biomarkers in lung cancer screening: technical considerations and feasibility assessment
    (Elsevier, 2024) Seijo, L. (Luis); Calle-Arroyo, C. (Carlos) de la; Pineda-Lucena, A. (Antonio); Detterbeck, F. (Frank); Bernasconi-Bisio, F. (Franco); Johansson, M. (Mattias); Montuenga-Badia, L.M. (Luis M.); Orive-Mauleón, D. (Daniel); Hung, J.R. (Rayjean); Valencia, K. (Karmele); Echepare, M. (Mirari); Robbins, H.A. (Hilary); Fernandez-Sanmamed, M. (Miguel)
    Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and the presence of metastases. Several countries around the world have adopted nation-wide LDCT-based lung cancer screening that will benefit patients, shifting the stage at diagnosis to earlier stages with more therapeutic options. Biomarkers can help to optimize the screening process, as well as refine the TNM stratification of lung cancer patients, providing information regarding prognostics and recommending management strategies. Moreover, novel adjuvant strategies will clearly benefit from previous knowledge of the potential aggressiveness and biological traits of a given early-stage surgically resected tumor. This review focuses on proteins as promising biomarkers in the context of lung cancer screening. Despite great efforts, there are still no successful examples of biomarkers in lung cancer that have reached the clinics to be used in early detection and early management. Thus, the field of biomarkers in early lung cancer remains an evident unmet need. A more specific objective of this review is to present an up-to-date technical assessment of the potential use of protein biomarkers in early lung cancer detection and management. We provide an overview regarding the benefits, challenges, pitfalls and constraints in the development process of protein-based biomarkers. Additionally, we examine how a number of emerging protein analytical technologies may contribute to the optimization of novel robust biomarkers for screening and effective management of lung cancer.
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    Optimal designs for Antoine equation
    (Elsevier, 2021) Calle-Arroyo, C. (Carlos) de la; López-Fidalgo, J. (Jesús); Rodríguez-Aragón, L.J. (Licesio J.)
    Vapor pressure is a temperature-dependent characteristic of pure liquids, and also of their mixtures. This thermodynamic property can be characterized through a wide range of models. Antoine Equation stands out among them for its simplicity and precision. Its parameters are estimated via maximum likelihood with experimental data. Once the parameters of the equation have been estimated, vapor pressures between known values of the curve can be interpolated. Other physical properties such as heat of vaporization can be predicted as well. This paper presents optimal designs to estimate the unknown parameters of the Antoine Equation as accurately as possible, considering a normal homoscedastic and heteroscedastic variance for the response. The aim is to improve the precision of inferences using optimality criteria to address different questions, such as fitting the whole model, focusing on some parameters of interest, or making predictions in a specific part of the space. In particular, the experimenter may choose between minimizing: the confidence region of the parameters, the variance of a subset of the parameters, the average of the variance of the parameters, or the variances of the predictions in a defined region. Optimal designs are often criticized by experimenters for their small number of experimental points. However, once the optimal designs are known, and given the idea of efficiency of a design, some strategies are presented here to improve their usual experimental designs. This study is complemented by an online tool that allows the user to replicate the calculations presented and extend them to any substance and temperature range.