Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson’s Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson’s Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson’s Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson’s ProgressionMarkers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of definedMorphological Features) fromPPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.This work was supported by the MINECO/FEDER under the TEC2015-64718-R project and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103

"The Ising model on spherical lattices: dimer versus Monte Carlo approach"

We study, using dimer and Monte Carlo approaches, the critical properties and finite size effects of the Ising model on honeycomb lattices folded on the tetrahedron. We show that the main critical exponents are not affected by the presence of conical singularities. The finite size scaling of the position of the maxima of the specific heat does not match, however, with the scaling of the correlation length, and the thermodynamic limit is attained faster on the spherical surface than in corresponding lattices on the torus.Comment: 25 pages + 6 figures not included. Latex file. FTUAM 93-2

Oligodendrocyte metabolism throughout its differentiation: immunocytochemistry study and its impact in remyelination

Introduction: Oligodendrocytes (OL) role in demyelinating pathologies such as multiple sclerosis and other neurodegenerative diseases is only recently being subject of extensive research. While the genetic and molecular aspects have been thoroughly studied, their metabolism was overshadowed. In order to develop new therapies to promote remyelination of already damaged axons, we need to accurately describe how OL metabolism affects axon myelination and trophic support (1). The objective of this study is to obtain cytological evidence of the extent of both glycolytic metabolism and oxidative phosphorylation by immunocytochemistry throughout the development of OL. Methods: Oligodendroglia cells from post-natal mice cortices were obtained and cultured. A wide assortment of differentiation-stage-specific cell surface antigens, a glycolytic and an oxidative phosphorylation marker were combined in several immunofluorescences to study both metabolic pathways in each step of differentiation. Results: After analysing them under confocal microscopy and imaging software, we observed a constant upregulation of glycolytic metabolism throughout differentiation, while oxidative phosphorylation seemed to increase with differentiation to then decrease when oligodendrocytes achieved their final maturation stage. Conclusions: Therefore, oxidative phosphorylation may be crucial in the differentiation of precursors and glycolysis would thus be the preferred metabolic pathway for fully matured OL. [1] Rosko L. et al. Neuroscientist. 2019;25(4):334–43.Supported by UMA and IBIMA and funding from two ongoing projects: - ‘Modulation of oligodendrocyte metabolism via blood vessel remodelling as target to promote remyelination’ (funding by NEURATRIS). - ‘Blood vessel remodelling modulates remyelination by oligodendrocyte metabolic reprogramming’ (funding by Arsep Foundation). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Gastrointestinal Stromal Tumor (GIST) and its relationship with germline mutations

We present the case of a 38-year-old man with a history of abdominal paraganglioma 10 years ago, who consulted for hematemesis and asthenia of 5 days' evolution. An upper gastrointestinal endoscopy was performed where a raised submucosal lesion, about 2 cm, with ulceration on its surface, was observed at the corporal-antral junction. The CT scan revealed nodular thickening of the gastric wall at the level of the lesser curvature. After the resolution of his hematemesis, it was decided to intervene on the patient, performing a partial gastrectomyUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Parkinson’s Disease Detection Using Isosurfaces-Based Features and Convolutional Neural Networks

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson’s disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.MINECO/FEDER under TEC2015-64718-R, PSI2015-65848-R, PGC2018-098813-B-C32, and RTI2018-098913-B-100 projects

Gastrointestinal cancer: Relationship between histology and microbiota

Este trabajo fue presentado como comunicación tipo póster en el citado congreso.Objectives: Review of the published literature concerning the relationship between microbiome and gastrointestinal cancer. Methods: Present work is focused on systematic research in the most prominent biomedical databases finds relevant works in Pubmed and the library’s catalog of the University of Málaga (Jábega) of published journals in the last 5 years. Results: In this work, the mechanisms used by the microbiome to damage gastrointestinal epithelial cells and cause cancer are explained. Some of them are the dysbiosis, destruction of the mucosal barrier, chronic inflammation, damage caused by metabolites produced in the digestion and the direct attack of certain toxins to the cell’s DNA. These mechanisms adjust the immune response, by activation or inhibition using different cytokines. There is also a deeper look into several microorganisms and how they cause gastrointestinal cancer using toxins or virulence factors to activate them. Conclusions: The evidence found so far about the microbiota and gastrointestinal cancer is enough to assume the relationship between them, although there is much left to research. With these findings, it can be expected that in a near future certain microorganisms could be used for screening purposes, due to their increase in early stages of the tumor genesis and also, in a preventive way to try to eradicate them, even avoid cancer. Studies on the microbiota are hardly beginning, and results appear to be promising.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning

Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient ' s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was determined by hypercholesterolemia and associated treatments, the second factor exhibited glycemic disorders and accompanying treatments and the third factor was characterized by hepatic enzymes. Subsequently four clusters of patients were identified, where cluster 1 was characterized by glucose disorders and treatments, cluster 2 presented mild MetS, cluster 3 presented exacerbated levels of hepatic enzymes and cluster 4 highlighted cholesterol and its associated treatments Interestingly, the liver status related cluster was characterized by higher protein consumption and cluster 4 with low polyunsaturated fatty acid intake. This research emphasized the potential clinical relevance of hepatic impairments in addition to MetS traditional characterization for precision and personalized management of MetS patients

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2