Machine learning for mortality analysis in patients with COVID-19

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.This research was funded by Agencia Estatal de Investigación AEI/FEDER Spain, Project PGC2018-095895-B-I00, and Comunidad Autónoma de Madrid, Spain, Project S2017/BMD-368

Interaction between angiotensin type 1, type 2, and mas receptors to regulate adult neurogenesis in the brain ventricular–subventricular zone

The renin–angiotensin system (RAS), and particularly its angiotensin type-2 receptors (AT2), have been classically involved in processes of cell proliferation and maturation during development. However, the potential role of RAS in adult neurogenesis in the ventricular-subventricular zone (V-SVZ) and its aging-related alterations have not been investigated. In the present study, we analyzed the role of major RAS receptors on neurogenesis in the V-SVZ of adult mice and rats. In mice, we showed that the increase in proliferation of cells in this neurogenic niche was induced by activation of AT2 receptors but depended partially on the AT2-dependent antagonism of AT1 receptor expression, which restricted proliferation. Furthermore, we observed a functional dependence of AT2 receptor actions on Mas receptors. In rats, where the levels of the AT1 relative to those of AT2 receptor are much lower, pharmacological inhibition of the AT1 receptor alone was sufficient in increasing AT2 receptor levels and proliferation in the V-SVZ. Our data revealed that interactions between RAS receptors play a major role in the regulation of V-SVZ neurogenesis, particularly in proliferation, generation of neuroblasts, and migration to the olfactory bulb, both in young and aged brains, and suggest potential beneficial effects of RAS modulators on neurogenesis.This research was funded by Spanish grants from Ministerio de Economía y Competitividad (BFU2015-70523 and SAF2017-86690-R), Instituto de Salud Carlos III (Retic TERCEL RD16/0011/0016, RD16/0011/0017, and CIBERNED), Galician Government (XUGA, ED431C2018/10; ED431G/05), FEDER (Regional European Development Fund), Generalitat Valenciana (Prometeo 2017-030), and Fundación Emilio Botín-Banco SantanderS

SAFARI optical system architecture and design concept

SpicA FAR infrared Instrument, SAFARI, is one of the instruments planned for the SPICA mission. The SPICA mission is the next great leap forward in space-based far-infrared astronomy and will study the evolution of galaxies, stars and planetary systems. SPICA will utilize a deeply cooled 2.5m-class telescope, provided by European industry, to realize zodiacal background limited performance, and high spatial resolution. The instrument SAFARI is a cryogenic grating-based point source spectrometer working in the wavelength domain 34 to 230 μm, providing spectral resolving power from 300 to at least 2000. The instrument shall provide low and high resolution spectroscopy in four spectral bands. Low Resolution mode is the native instrument mode, while the high Resolution mode is achieved by means of a Martin-Pupplet interferometer. The optical system is all-reflective and consists of three main modules; an input optics module, followed by the Band and Mode Distributing Optics and the grating Modules. The instrument utilizes Nyquist sampled filled linear arrays of very sensitive TES detectors. The work presented in this paper describes the optical design architecture and design concept compatible with the current instrument performance and volume design drivers

The Raman Laser Spectrometer for the ExoMars Rover Mission to Mars

The Raman Laser Spectrometer (RLS) on board the ESA/Roscosmos ExoMars 2020 mission will provide precise identification of the mineral phases and the possibility to detect organics on the Red Planet. The RLS will work on the powdered samples prepared inside the Pasteur analytical suite and collected on the surface and subsurface by a drill system. Raman spectroscopy is a well-known analytical technique based on the inelastic scattering by matter of incident monochromatic light (the Raman effect) that has many applications in laboratory and industry, yet to be used in space applications. Raman spectrometers will be included in two Mars rovers scheduled to be launched in 2020. The Raman instrument for ExoMars 2020 consists of three main units: (1) a transmission spectrograph coupled to a CCD detector; (2) an electronics box, including the excitation laser that controls the instrument functions; and (3) an optical head with an autofocus mechanism illuminating and collecting the scattered light from the spot under investigation. The optical head is connected to the excitation laser and the spectrometer by optical fibers. The instrument also has two targets positioned inside the rover analytical laboratory for onboard Raman spectral calibration. The aim of this article was to present a detailed description of the RLS instrument, including its operation on Mars. To verify RLS operation before launch and to prepare science scenarios for the mission, a simulator of the sample analysis chain has been developed by the team. The results obtained are also discussed. Finally, the potential of the Raman instrument for use in field conditions is addressed. By using a ruggedized prototype, also developed by our team, a wide range of terrestrial analog sites across the world have been studied. These investigations allowed preparing a large collection of real, in situ spectra of samples from different geological processes and periods of Earth evolution. On this basis, we are working to develop models for interpreting analog processes on Mars during the mission. Key Words: Raman spectroscopy—ExoMars mission—Instruments and techniques—Planetary sciences—Mars mineralogy and geochemistry—Search for life on Mars. Astrobiology 17, 627–65

Contaminants of emerging concern in freshwater fish from four Spanish Rivers

This study investigated the occurrence of 135 contaminants of emerging concern (CECs) – pharmaceuticals, pesticides, a set of endocrine disrupting compounds (EDCs) (parabens, bisphenols, hormones, triazoles, organophosphorus flame retardants and triclosan), UV-filters, perfluoroalkyl substances (PFASs) and halogenated flame retardants (HFRs) – in 59 fish samples, collected in 2010 in 4 Spanish Rivers (Guadalquivir, Júcar, Ebro and Llobregat). Of the 135 CECs, 76 including 8 pharmaceuticals, 25 pesticides, 10 EDCs, 5 UV-filters, 15 PFASs and 13 HFRs were detected. Pharmaceuticals were the less frequently found and at lower concentrations. Pesticides, EDCs, UV-filters, PFASs and HFRs were detected more frequently (>50% of the samples). The maximum concentrations were 15 ng/g dry weight (dw) for pharmaceuticals (diclofenac), 840 ng/g dw for pesticides (chlorpyrifos), 224 ng/g dw for EDCs (bisphenol A), 242 ng/g dw for UV-filters (EHMC), 1738 ng/g dw for PFASs (PFHxA) and 64 ng/g dw for HFRs (Dec 602). The contaminants detected in fish are commonly detected also in sediments. In light of current knowledge, the risk assessment revealed that there was no risk for humans related to the exposure to CECs via freshwater fish consumption. However, results provide detailed information on the mixtures of CECs accumulated that would be very useful to identify their effects on aquatic biota.This research has been supported by the European Union 7th Framework Programme funding under Grant Agreement No. 603629-ENV-2013-6.2.1-Globaqua, by the Generalitat de Catalunya (Consolidated Research Groups 2017 SGR 1404 - Water and Soil Quality Unit) and by the Generalitat Valenciana (ANTROPOCEN@, PROMETEO/2018/155).Peer reviewe

Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3–6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin’s lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research. We thank the Spanish Society of Hematology (SEHH) for its support on the study. We sincerely want to thanks the invaluable aid of microbiology services for their commitment in SARS-CoV-2-reactive IgG antibody monitoring in these highly immunosuppressed patients from all participating centers. Finally, we also want to thank the patients, nurses, and study coordinators for their foremost contributions in this study.Peer reviewe

Integrating mechanistic and toxicokinetic information in predictive models of cholestasis

Data de publicació electrònica: 03-09-2023Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, European Union’s Horizon 2020, and EFPIA. The authors declare that this work reflects only the author’s view and that IMI-JU is not responsible for any use that may be made of the information it contains. Also, this project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 964537 (RISK-HUNT3R), which is part of the ASPIS cluster

Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models

[EN] These datasets were generated to train ("PopulationDrugsTrainingKrNaLCaL.xlsx") and test ("PopulationDrugsTestKrCaLNaL.xlsx") the uncertainty assessment models developed in the paper "Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models" by Kopanska, Rodríguez-Belenguer, et al.The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, supported from European Union's Horizon 2020 and the EFPIA. We also received funding from the SimCardioTest supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the “Formacion de Profesorado Universitario” (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/ 2020/043).Kopanska, K.; Rodríguez-Belenguer, P.; Llopis-Lorente, J.; Trénor, B.; Saiz, J.; Pastor, M. (2023). Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/19182

Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

In cardiotoxicity studies it is common to pre-compute the values of different biomarkers (my equation or TX) for a range of ion channel blockades. Since every simulation requires costly computations, to complete the matrix of simulations for several ion channels can be cumbersome. Some examples of how these simulations are run and used are included in the references. The relationship between the input values and the biomarker is not too complex and Machine Learning can be used to obtain a good approximation. The resulting function can be generated using only an small fraction of the computations required to generate the whole matrix. This function can then be used to predict the biomarker value for any combination of the covered range, with an excellent accuracy In this repository we have included a jupyter notebook and some simulation results that demonstrate this idea. Regarding the data matrices, they correspond to simulations using a modified version of the ventricular action potential model by O'Hara et al., which have been performed by Jordi Llopis, Beatriz Trenor and Javier Saiz at the Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain KrKsCaL.xlsx: This is the data matrix needed to build the ML models. APD90_12CiPA_drugs_IKrIKsICaL.xlsx: This excel file contains the input and output values for CiPA compounds. EFTPC_IC50_28_CiPADrugs.xlsx: This file contains D, my equation and hill coefficient to calculate the input values for CiPA compounds of the previous excel file. Folder "Matrix Building": This folder contains MATLAB functions for generating the KrKsCaL matrix. The script "buildMatrixKrKsNaL.m" is the main script which run the electrophysioloigcal simulations and generates the matrix References Llopis J, Cano J, Gomis-Tena J, Romero L, Sanz F, Pastor M, Trenor B, Saiz J. In silico assay for preclinical assessment of drug proarrhythmicity. J Pharmacol Toxicol Methods 2019 99: 106595. PMID: 31962986 DOI: 10.1016/j.vascn.2019.05.106. O’Hara, T., Virág, L., Varró, A. & Rudy, Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLOS Comput. Biol. 7, e1002061 (2011). Licensing CardioML was produced at the PharmacoInformatics lab (http://phi.upf.edu), in the framework of the eTRANSAFE project (http://etransafe.eu). eTRANSAFE has received support from IMI2 Joint Undertaking under Grant Agreement No. 777365. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Copyright 2022 Manuel Pastor ([email protected]) CardioML is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 3. CardioML is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with CardioML source code. If not, see http://www.gnu.org/licenses/.Rodríguez-Belenguer, P.; Kopańska, K.; Llopis Lorente, J.; Trénor Gomis, BA.; Saiz Rodríguez, FJ.; Pastor, M. (2022). Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia. http://hdl.handle.net/10251/18306

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.The authors received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 964537 (RISK-HUNT3R), which is part of the ASPIS cluster