Analysis of the influence of educational level on the nutritional status and lifestyle habits of the young Spanish population.

Aim: This study aims to analyze some nutrition and health habits of young people and the impact of educational attainment on health. Methods: An observational, descriptive, and cross-sectional study was carried out using surveys. Using non-probabilistic snowball sampling, a previously validated questionnaire was disseminated through networks, collecting a sample of 9,681 people between 18 and 30 years old. Comparative analyses between groups were obtained by clustering and the corresponding statistical tests. Results: The results showed how young people with higher education generally have a lower BMI, a higher healthy nutrition index, less frequent consumption of sugary drinks, and less smoking than their peers with basic education. These healthier habits are reflected in the higher self-perceived health status of the higher-educated group. While for all the educational levels analyzed, the minutes of physical activity practice are above the 150 min recommended by the WHO. Conclusion: Our findings suggest that young people's education level is of fundamental importance for health, particularly for nutritional habits. In general, the lifestyle habits of the young Spanish population are healthy, but there is a need for improvement in those aspects related to nutrition and food.

Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis.

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data.

Single-cell RNA sequencing is the reference technology to characterize the composition of the tumor microenvironment and to study tumor heterogeneity at high resolution. Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single-cell RNA-seq data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to datasets encompassing 106 samples and 93,322 cells from different tumor types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.

Synthesis of Quinolones and Zwitterionic Quinolonate Derivatives with Broad-Spectrum Antibiotic Activity.

Quinolones are one of the most extensively used therapeutic families of antibiotics. However, the increase in antibiotic-resistant bacteria has rendered many of the available compounds useless. After applying our prediction model of activity against E. coli to a library of 1000 quinolones, two quinolones were selected to be synthesized. Additionally, a series of zwitterionic quinolonates were also synthesized. Quinolones and zwitterionic quinolonates were obtained by coupling the corresponding amine with reagent 1 in acetonitrile. Antibacterial activity was assessed using a microdilution method. All the compounds presented antibacterial activity, especially quinolones 2 and 3, selected by the prediction model, which had broad-spectrum activity. Furthermore, a new type of zwitterionic quinolonate with antibacterial activity was found. These compounds can lead to a new line of antimicrobials, as the structures, and, therefore, their properties, are easily adjustable in the amine in position 4 of the pyridine ring.

New Pharmacokinetic and Microbiological Prediction Equations to Be Used as Models for the Search of Antibacterial Drugs.

Currently, the development of resistance of Enterobacteriaceae bacteria is one of the most important health problems worldwide. Consequently, there is a growing urge for finding new compounds with antibacterial activity. Furthermore, it is very important to find antibacterial compounds with a good pharmacokinetic profile too, which will lead to more efficient and safer drugs. In this work, we have mathematically described a series of antibacterial quinolones by means of molecular topology. We have used molecular descriptors and related them to various pharmacological properties by using multilinear regression (MLR) analysis. The regression functions selected by presenting the best combination of a number of quality and validation metrics allowed for the reliable prediction of clearance (CL), and minimum inhibitory concentration 50 against Enterobacter aerogenes (MIC50Ea) and Proteus mirabilis (MIC50Pm). The obtained results clearly reveal that the combination of molecular topology methods and MLR provides an excellent tool for the prediction of pharmacokinetic properties and microbiological activities in both new and existing compounds with different pharmacological activities.

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study.

A major risk factor of COVID-19 severity is the patient's health status at the time of the infection. Numerous studies focused on specific chronic diseases and identified conditions, mainly cardiovascular ones, associated with poor prognosis. However, chronic diseases tend to cluster into patterns, each with its particular repercussions on the clinical outcome of infected patients. Network analysis in our population revealed that not all cardiovascular patterns have the same risk of COVID-19 hospitalization or mortality and that this risk depends on the pattern of multimorbidity, besides age and sex. We evidenced that negative outcomes were strongly related to patterns in which diabetes and obesity stood out in older women and men, respectively. In younger adults, anxiety was another disease that increased the risk of severity, most notably when combined with menstrual disorders in women or atopic dermatitis in men. These results have relevant implications for organizational, preventive, and clinical actions to help meet the needs of COVID-19 patients.

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm.

The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry-many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry-virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.

Monitoring Weeder Robots and Anticipating Their Functioning by Using Advanced Topological Data Analysis.

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the "shape" of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis.

Chronic diseases associated with increased likelihood of hospitalization and mortality in 68,913 COVID-19 confirmed cases in Spain: A population-based cohort study.

BACKGROUND: Clinical outcomes among COVID-19 patients vary greatly with age and underlying comorbidities. We aimed to determine the demographic and clinical factors, particularly baseline chronic conditions, associated with an increased risk of severity in COVID-19 patients from a population-based perspective and using data from electronic health records (EHR). METHODS: Retrospective, observational study in an open cohort analyzing all 68,913 individuals (mean age 44.4 years, 53.2% women) with SARS-CoV-2 infection between 15 June and 19 December 2020 using exhaustive electronic health registries. Patients were followed for 30 days from inclusion or until the date of death within that period. We performed multivariate logistic regression to analyze the association between each chronic disease and severe infection, based on hospitalization and all-cause mortality. RESULTS: 5885 (8.5%) individuals showed severe infection and old age was the most influencing factor. Congestive heart failure (odds ratio -OR- men: 1.28, OR women: 1.39), diabetes (1.37, 1.24), chronic renal failure (1.31, 1.22) and obesity (1.21, 1.26) increased the likelihood of severe infection in both sexes. Chronic skin ulcers (1.32), acute cerebrovascular disease (1.34), chronic obstructive pulmonary disease (1.21), urinary incontinence (1.17) and neoplasms (1.26) in men, and infertility (1.87), obstructive sleep apnea (1.43), hepatic steatosis (1.43), rheumatoid arthritis (1.39) and menstrual disorders (1.18) in women were also associated with more severe outcomes. CONCLUSIONS: Age and specific cardiovascular and metabolic diseases increased the risk of severe SARS-CoV-2 infections in men and women, whereas the effects of certain comorbidities are sex specific. Future studies in different settings are encouraged to analyze which profiles of chronic patients are at higher risk of poor prognosis and should therefore be the targets of prevention and shielding strategies.

Circadian PERformance in breast cancer: a germline and somatic genetic study of PER3VNTR polymorphisms and gene co-expression.

Polymorphisms in the PER3 gene have been associated with several human disease phenotypes, including sleep disorders and cancer. In particular, the long allele of a variable number of tandem repeat (VNTR) polymorphism has been previously linked to an increased risk of breast cancer. Here we carried out a combined germline and somatic genetic analysis of the role of the PER3VNRT polymorphism in breast cancer. The combined data from 8284 individuals showed a non-significant trend towards increased breast cancer risk in the 5-repeat allele homozygous carriers (OR = 1.17, 95% CI: 0.97-1.42). We observed allelic imbalance at the PER3 locus in matched blood and tumor DNA samples, showing a significant retention of the long variant (risk) allele in tumor samples, and a preferential loss of the short repetition allele (p = 0.0005). Gene co-expression analysis in healthy and tumoral breast tissue samples uncovered significant associations between PER3 expression levels with those from genes which belong to several cancer-associated pathways. Finally, relapse-free survival (RFS) analysis showed that low expression levels of PER3 were linked to a significant lower RSF in luminal A (p = 3 × 10-12) but not in the rest of breast cancer subtypes.

Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework.

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

Transcriptomic and Genetic Associations between Alzheimer's Disease, Parkinson's Disease, and Cancer.

Alzheimer's (AD) and Parkinson's diseases (PD) are the two most prevalent neurodegenerative disorders in human populations. Epidemiological studies have shown that patients suffering from either condition present a reduced overall risk of cancer than controls (i.e., inverse comorbidity), suggesting that neurodegeneration provides a protective effect against cancer. Reduced risks of several site-specific tumors, including colorectal, lung, and prostate cancers, have also been observed in AD and PD. By contrast, an increased risk of melanoma has been described in PD patients (i.e., direct comorbidity). Therefore, a fundamental question to address is whether these associations are due to shared genetic and molecular factors or are explained by other phenomena, such as flaws in epidemiological studies, exposure to shared risk factors, or the effect of medications. To this end, we first evaluated the transcriptomes of AD and PD post-mortem brain tissues derived from the hippocampus and the substantia nigra and analyzed their similarities to those of a large panel of 22 site-specific cancers, which were obtained through differential gene expression meta-analyses of array-based studies available in public repositories. Genes and pathways that were deregulated in both disorders in each analyzed pair were examined. Second, we assessed potential genetic links between AD, PD, and the selected cancers by establishing interactome-based overlaps of genes previously linked to each disorder. Then, their genetic correlations were computed using cross-trait LD score regression and GWAS summary statistics data. Finally, the potential role of medications in the reported comorbidities was assessed by comparing disease-specific differential gene expression profiles to an extensive collection of differential gene expression signatures generated by exposing cell lines to drugs indicated for AD, PD, and cancer treatment (LINCS L1000). We identified significant inverse associations of transcriptomic deregulation between AD hippocampal tissues and breast, lung, liver, and prostate cancers, and between PD substantia nigra tissues and breast, lung, and prostate cancers. Moreover, significant direct (same direction) associations of deregulation were observed between AD and PD and brain and thyroid cancers, as well as between PD and kidney cancer. Several biological processes, including the immune system, oxidative phosphorylation, PI3K/AKT/mTOR signaling, and the cell cycle, were found to be deregulated in both cancer and neurodegenerative disorders. Significant genetic correlations were found between PD and melanoma and prostate cancers. Several drugs indicated for the treatment of neurodegenerative disorders and cancer, such as galantamine, selegiline, exemestane, and estradiol, were identified as potential modulators of the comorbidities observed between neurodegeneration and cancer.

Molecular Topology for the Search of New Anti-MRSA Compounds.

The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DFMRSA1, DFMRSA2, DFMRSA3 and DFMRSA4 (DFMRSA was built in a previous study), all with good statistical parameters, such as Fisher-Snedecor F (>68 in all cases), Wilk's lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.

A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework.

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets.

MOTIVATION: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. RESULTS: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. AVAILABILITY AND IMPLEMENTATION: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against Escherichia coli.

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

Molecular Topology for the Discovery of New Broad-Spectrum Antibacterial Drugs.

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four equations were constructed, named DF1, DF2, DF3, and DF4, all with good statistical parameters such as Fisher-Snedecor's F (over 25 in all cases), Wilk's lambda (below 0.36 in all cases) and percentage of correct classification (over 80% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. From the four discriminant functions, it can be extracted that the presence of sp3 carbons, ramifications, and secondary amine groups in a molecule enhance antibacterial activity, whereas the presence of 5-member rings, sp2 carbons, and sp2 oxygens hinder it. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of antibacterial activity.

Baseline Chronic Comorbidity and Mortality in Laboratory-Confirmed COVID-19 Cases: Results from the PRECOVID Study in Spain.

We aimed to analyze baseline socio-demographic and clinical factors associated with an increased likelihood of mortality in men and women with coronavirus disease (COVID-19). We conducted a retrospective cohort study (PRECOVID Study) on all 4412 individuals with laboratory-confirmed COVID-19 in Aragon, Spain, and followed them for at least 30 days from cohort entry. We described the socio-demographic and clinical characteristics of all patients of the cohort. Age-adjusted logistic regressions models were performed to analyze the likelihood of mortality based on demographic and clinical variables. All analyses were stratified by sex. Old age, specific diseases such as diabetes, acute myocardial infarction, or congestive heart failure, and dispensation of drugs like vasodilators, antipsychotics, and potassium-sparing agents were associated with an increased likelihood of mortality. Our findings suggest that specific comorbidities, mainly of cardiovascular nature, and medications at the time of infection could explain around one quarter of the mortality in COVID-19 disease, and that women and men probably share similar but not identical risk factors. Nonetheless, the great part of mortality seems to be explained by other patient- and/or health-system-related factors. More research is needed in this field to provide the necessary evidence for the development of early identification strategies for patients at higher risk of adverse outcomes.