Desarrollo de un algoritmo de visión artificial: un enfoque a la identificación y evaluación temprana de heridas de pie diabético

Diabetic foot is one of the most devastating consequences related to diabetes. It is relevant because of its incidence and the elevated percentage of amputations and deaths that the disease implies. Given the fact that the existing tests and laboratories designed to diagnose it are limited and expensive, the most common evaluation is still based on signs and symptoms. This means that the specialist completes a questionnaire based solely on observation and an invasive wound measurement. Using the questionnaire, the physician issues a diagnose. In this sense, the diagnose relies only on the criteria and the specialists’ experience. For some variables such as the lesions area or their location, this dependency is not acceptable. Currently bioengineering has played a key role on the diagnose of different chronic degenerative diseases. A timely diagnose has proven to be the best tool against diabetic foot. The diabetic’s foot clinical evaluation increases the possibility to identify risks and further complications. The main goal of this paper is to present the development of an algorithm based on digital image processing techniques, which enables to optimize the results on the diabetic’s foot lesion evaluation. Using advanced techniques for object segmentation and adjusting the sensibility parameter, allows the correlation between the algorithm’s identified wounds and those observed by the physician. Using the developed algorithm it is possible to identify and asses the wounds, their size, location, in a noninvasive way.El pie diabético es una de las complicaciones más devastadoras de la diabetes. Su trascendencia es significativa por su alta incidencia y por el elevado porcentaje de amputaciones y decesos que implica. Dado que las pruebas de laboratorio para su diagnóstico son limitadas y costosas, la evaluación típica sigue basándose en signos y síntomas. Esto es, el médico llena un cuestionario basado en la medición de sus instrumentos de apoyo y su propia observación. Con base en tal cuestionario emite un diagnóstico. En el sentido de lo anterior, se tiene un diagnóstico dependiente del criterio y experiencia del evaluador. Para algunas variables, como el área de las lesiones o la ubicación de éstas, esta dependencia no es aceptable. El presente trabajo pretende, mediante la introducción de técnicas de procesamiento de imágenes digitales, convertirse en un primer eslabón para la optimización de los resultados en la evaluación del pie diabético. La aplicación del algoritmo sobre un grupo de imágenes de prueba dio resultados aceptables en la detección de las heridas, así como su tamaño y ubicación, gracias al empleo de técnicas avanzadas de segmentación de objetos y al parámetro que permite ajustar la sensibilidad del sistema hasta obtener los resultados deseados. La aportación de esta tesis es un sistema de evaluación de lesiones del pie diabético que, sin duda, puede ser una herramienta muy útil para el especialista que permite la detección automática de las lesiones y la extracción de sus características de forma no invasiva, además de facilitar el manejo de los datos de forma digital

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

Identification of a new candidate locus for ebstein anomaly in 1p36.2

Ebstein anomaly (EA) is a rare congenital heart defect (CHD) with a poorly characterized genetic etiology. However, some EA patients carry deletions in 1p36, all of which have been reported to carry distal deletions and share loss of the PRDM16 gene, which is currently considered the most likely candidate for EA development in this region. Here, we report a patient with an 11.96-Mb proximal 1p36 deletion, without loss of PRDM16, who presented with EA and a proximal deletion phenotype. This finding suggests that PRDM16 loss is not required for the development of EA in 1p36 deletions and that the loss of an additional proximal locus in 1p36 is also likely associated with EA. Our data suggest that a distal locus containing the SKI gene and a proximal locus containing the CHD-associated genes RERE and UBE4B are the most probable etiological factors for EA in patients with 1p36 deletion syndrome. © 2018 S. Karger AG, Basel. All rights reserved

Decision Making using Internet of Things and Machine Learning: A bibliometric approach to tracking main research themes

The internet has become an integral part of our life, we connect daily for informational, social, entertainment and even work and economic purposes. Therefore, it is completely normal to connect to the internet; But it is not only people, also these everyday objects or things in our environment who connect to the network to take advantage of its benefits. This next stage in the evolution of the internet, in which connectivity extends to the objects which surround us, is known as the Internet of Things. Sensors, Machines, Devices, Wearables are part of the Internet of Things and these can be identified to be recognized and interact without the need for a human being. With the Internet of Things, the connection extends to any type of object. All these interconnected devices and people produce an immense amount of data, when analyzed, they become a tool that allows organizations to make wise decisions in real time by having information on various market situations and ensuring a better understanding of behavior and consumer preferences. One of the disciplines of Artificial Intelligence responsible for analyzing this large amount of data is Machine Learning, whose sole purpose is to improve the Decision-Making process. To understand the relationship between Internet of Things, application of Machine Learning and the Decision Making process, a bibliometric analysis is carried out from 2013 to 2020 to know its evolution, trends, research areas, authors and other aspects using Scopus

Assessing the viability of electro-absorption and photoelectro-absorption for the treatment of gaseous perchloroethylene

This work focuses on the development of electro-absorption and photoelectro-absorption technologies to treat gases produced by a synthetic waste containing the highly volatile perchloroethylene (PCE). To do this, a packed absorption column coupled with a UV lamp and an undivided electrooxidation cell was used. Firstly, it was confirmed that the absorption in a packed column is a viable method to achieve retention of PCE into an absorbent-electrolyte liquid. It was observed that PCE does not only absorb but it was also transformed into phosgene and other by-products. Later, it was confirmed that the electro-absorption process influenced the PCE degradation, favoring the transformation of phosgene into final products. Opposite to what is expected, carbon dioxide is not the main product obtained, but carbon tetrachloride and trichloroacetic acid. Both species are also hazardous but their higher solubility in water opens possibilities for a successful and more environmental-friendly removal. The coupling with UV-irradiation has a negative impact on the degradation of phosgene. Finally, a reaction mechanism was proposed for the degradation of PCE based on the experimental observations. Results were not as expected during the planning of the experimental work but it is important to take in mind that PCE decomposition occurs in wet conditions, regardless of the applied technology, and this work is a first approach to try to solve the treatment problems associated to PCE gaseous waste flows in a realistic way

Animal Models of Rheumatoid Arthritis

Autoimmunity is a condition in which the host organizes an immune response against its own antigens. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, characterized by the presence of chronic inflammatory infiltrates, the development of destructive arthropathy, bone erosion, and degradation of the articular cartilage and subchondral bone. There is currently no treatment that resolves the disease, only the use of palliatives, and not all patients respond to pharmacologic therapy. According to RA multifactorial origin, several in vivo models have been used to evaluate its pathophysiology as well as to identify the usefulness of biomarkers to predict, to diagnose, or to evaluate the prognosis of the disease. This chapter focuses on the most common in vivo models used for the study of RA, including those related with genetic, immunological, hormonal, and environmental interactions. Similarly, the potential of these models to understand RA pathogenesis and to test preventive and therapeutic strategies of autoimmune disorder is also highlighted. In conclusion, of all the animal models discussed, the CIA model could be considered the most successful by generating arthritis using type II collagen and adjuvants and evaluating therapeutic compounds both intra-articularly and systemically

Biological Activity and Implications of the Metalloproteinases in Diabetic Foot Ulcers

Inadequate metabolic control predisposes diabetic patient to a series of complications on account of diabetes mellitus (DM). Among the most common complications of DM is neuropathy, which causes microvascular damage by hyperglycemia in the lower extremities which arrives characterized by a delayed closing. The global prevalence of diabetic neuropathy (DN) was 66% of people with diabetes in 2015, representing the principal cause of total or partial lower extremities amputation, with 22.6% of the patients with DN. Matrix metalloproteinases (MMPs) are involved in healing. The function that these mainly play is the degradation during inflammation that has as consequence the elimination of the extracellular matrix (ECM), the disintegration of the capillary membrane to give way to angiogenesis and cellular migration for the remodeling of damaged tissue. The imbalance in MMPs may increase the chronicity of a wound, what leads to chronic foot ulcers and amputation. This chapter focuses on the role of MMPs in diabetic wound healing

Role of the IL33 and IL1RL1 pathway in the pathogenesis of Immunoglobulin A vasculitis

Cytokines signalling pathway genes are crucial factors of the genetic network underlying the pathogenesis of Immunoglobulin-A vasculitis (IgAV), an inflammatory vascular condition. An influence of the interleukin (IL)33- IL1 receptor like (IL1RL)1 signalling pathway on the increased risk of several immune-mediated diseases has been described. Accordingly, we assessed whether the IL33-IL1RL1 pathway represents a novel genetic risk factor for IgAV. Three tag polymorphisms within IL33 (rs3939286, rs7025417 and rs7044343) and three within IL1RL1 (rs2310173, rs13015714 and rs2058660), that also were previously associated with several inflammatory diseases, were genotyped in 380 Caucasian IgAV patients and 845 matched healthy controls. No genotypes or alleles differences were observed between IgAV patients and controls when IL33 and IL1RL1 variants were analysed independently. Likewise, no statistically significant differences were found in IL33 or IL1RL1 genotype and allele frequencies when IgAV patients were stratified according to the age at disease onset or to the presence/absence of gastrointestinal (GI) or renal manifestations. Similar results were disclosed when IL33 and IL1RL1 haplotypes were compared between IgAV patients and controls and between IgAV patients stratified according to the clinical characteristics mentioned above. Our results suggest that the IL33-IL1RL1 signalling pathway does not contribute to the genetic network underlying IgAV.Acknowledgements: We are indebted to the patients and healthy controls for their essential collaboration to this study. We also thank the National DNA Bank Repository (Salamanca) for supplying part of the control samples. This study was supported by European Union FEDER funds and `Fondo de Investigaciones Sanitarias´ (Grant PI18/00042) from ‘Instituto de Salud Carlos III’ (ISCIII, Health Ministry, Spain). DP-P is a recipient of a Río Hortega programme fellowship from the ISCIII, co-funded by the European Social Fund (ESF, `Investing in your future´) (Grant Number CM20/00006). SR-M is supported by funds of the RETICS Program (RD16/0012/0009) (ISCIII, cofunded by the European Regional Development Fund (ERDF)). VP-C is supported by a pre-doctoral grant from IDIVAL (PREVAL 18/01). BA-M is a recipient of a `López Albo´ Post-Residency Programme funded by Servicio Cántabro de Salud. LL-G is supported by funds from IDIVAL (INNVAL20/06). OG is staff personnel of Xunta de Galicia (Servizo Galego de Saude (SERGAS)) through a research-staff stabilization contract (ISCIII/SERGAS) and his work is funded by ISCIII and the European Union FEDER fund (Grant Numbers RD16/0012/0014 (RIER) and PI17/00409). He is beneficiary of project funds from the Research Executive Agency (REA) of the European Union in the framework of MSCA-RISE Action of the H2020 Programme, project 734899—Olive-Net. RL-M is a recipient of a Miguel Servet type I programme fellowship from the ISCIII, co-funded by ESF (`Investing in your future´) (Grant Number CP16/00033)

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men