Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

Computational methods for breath metabolomics in clinical diagnostics

For a long time, human odors and vapors have been known for their diagnostic power. Therefore, the analysis of the metabolic composition of human breath and odors creates the opportunity for a non-invasive tool for clinical diagnostics. Innovative analytical technologies to capture the metabolic profile of a patient’s breath are available, such as, for instance, the ion mobility spectrometry coupled to a multicapilary collumn. However, we are lacking automated systems to process, analyse and evaluate large clinical studies of the human exhaled air. To fill this gap, a number of computational challenges need to be addressed. For instance, breath studies generate large amounts of heterogeneous data that requires automated preprocessing, peak-detection and identification as a basis for a sophisticated follow up analysis. In addition, generalizable statistical evaluation frameworks for the detection of breath biomarker profiles that are robust enough to be employed in routine clinical practice are necessary. In particular since breath metabolomics is susceptible to specific confounding factors and background noise, similar to other clinical diagnostics technologies. Moreover, spesific manifestations of disease stages and progression, may largely influence the breathomics profiles. To this end, this thesis will address these challenges to move towards more automatization and generalization in clinical breath research. In particular I present methods to support the search for biomarker profiles that enable a non-invasive detection of diseases, treatment optimization and prognosis to provide a new powerful tool for precision medicine.Seit jeher ist bekannt, dass Körpergeruch und der Atem Hinweise zu deren Gesundheitszustand liefern können. Eine Analyse der Atemluft auf molekularer Ebene verspricht daher neue Ansätze zur Diagnose spezifischer Krankheiten. Innovative Technologien wie die Ionen Mobilitäts Spectrometrie in Kombination mit einer Multikapilarsäule, erlauben erstmals hochauflösende metabolische Profile der Atemluft innerhalb kürzester Zeit zu erzeugen. Zur Zeit fehlen jedoch die notwendigen computergestützten Applikationen zur automatischen Organisation und Auswertung der generierten Daten. Eine besondere Herausforderung stellen dabei die großen Mengen heterogenener klinischer und analytischer Daten und deren Verarbeitung. Ähnlich wie andere Hochdurchsatzverfahren unterliegt die Atemluft dem Einfluss von Hintergrundsignalen wie der Umgebungsluft oder Anderen die Ergebnisse verzerrenden Faktoren, wie zum Beispiel Ernährung, Lebensgewohnheiten oder Medikation. Dies erfordert den Einsatz von modernen Methoden der Statistik und des maschinellen Lernens, um robuste und generalisierbare Krankheitsmarker zu identifizieren. Ein besonderer Augenmerk gilt hierbei auch Krankheiten deren metabolischer Fingerabdruck sich im Krankheitsverlauf drastisch verändern können. Das Ziel meiner Arbeit ist es Lösungen für die beschriebenen Probleme zu finden und damit die Suche nach praxistauglichen Krankheitsmarkern mit bioinformatischen Methoden zu unterstützen. Im Rahmen mehrerer Studien und Softwareprojekten wurden grundlegende Methodiken vorgestellt, evaluiert und etabliert, insbesondere im Hinblick auf die Entwicklung computergestützter Systeme zur automatischen Analyse von Atemluftdaten. Die vorgestellten Verfahren legen den Grundstein für die nicht invasive Detektion von Krankheiten, Optimierung und Prognose von Behandlungen und darüber hinaus für ein weiteres Werkzeug der personalisierten Medizin

Volatile diagnostic techniques for ventilator associated pneumonia

Ventilator associated pneumonia (VAP) is a significant challenge for the Intensive Care doctors worldwide. It is both difficult to diagnose accurately and quickly and to treat effectively once the diagnosis has been established. Current diagnostic microbiological methods of diagnosis can take up to 48 hours to yield results. Early diagnosis and treatment remain the best way of improving outcome for patients with VAP. In this study we look at novel diagnostic techniques for VAP. Electronic nose (Enose) technology was used to identify to identify the presence of microorganisms in bronchoalveolar lavage (BAL) fluid samples taken from the respiratory tracts of ventilated patients. The results were compared with standard microbiological culture and sensitivities. The Enose was able to discriminate 83% of samples into growth or no growth groups on samples grown in the lab. When the technique was employed to samples taken directly from patients the accuracy fell to 68.2%. This suggests that patient related factors may be reducing the accuracy of the Enose classification. The use of antimicrobial drugs prior to patient sampling is likely to have played a major role. The second part of this study used Gas Chromatography-Mass Spectrometry (GC-MS) analysis of patient’s breath in an attempt to identify patients with VAP. Breath samples were taken at the same time as the bronchoalveolar lavage samples described above. The use of this technique did show differences between the breath samples of patients who did not have any microbiological growth from their BAL samples and those that did. Leave one out cross validation of a PC fed LDA model showed 84% correct classification between healthy volunteers, no growth and growth groups. Finally, we evaluated the Breathotron, which is a breath analysis device designed and built at Cranfield Health. It allows for analysis of breath samples using a single sensor system as opposed to a sensor array employed in traditional Enose devices. This allows it to be more portable and cheaper to build. The Breathotron also allows collection of breath onto sorbent cartridges for GC-MS analysis. Its single sensor did not allow for accurate discrimination between samples.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

Metabolomics contributions to targeted and untargeted clinical analysis by chromatography and mass spectrometry

La investigación desarrollada en esta Tesis Doctoral se centró en realizar contribuciones en el ámbito del análisis clínico a través de estrategias metabolómicas tanto orientadas como globales en diferentes tipos de muestras biológicas mediante cromatografía de líquidos (LC) y espectrometría de masas (MS). Para ello primeramente se revisó exhaustiva y críticamente la bibliografía para conocer el estado del tratamiento de la orina como muestra y de los métodos de análisis de aire exhalado condensado, dos de los biofluidos utilizados en el desarrollo de esta Tesis. Además, se optimizaron métodos de análisis orientado para mejorar la cuantificación tanto de compuestos con potencial biomarcador como de fármacos y sus metabolitos para su aplicación en el diagnóstico y seguimiento de enfermedades o tratamientos. También se analizaron de forma global biofluidos para, (a) estudiar y optimizar el tratamiento de una muestra escasamente utilizada en el área clínica (el aire exhalado condensado –EBC), (b) identificar metabolitos con potencial predictivo para ayudar en el diagnóstico del cáncer de próstata utilizando la orina como muestra, y (c) mejorar y acelerar el tratamiento de datos a través de herramientas quimiométricas desarrolladas para combinar en una única matriz los datos obtenidos mediante ionización positiva y negativa en espectrometría de masas

Développement de langue électronique : étude de mélanges complexes et de bactéries

The objective of this PhD thesis is to explore the potential applications of the electronic tongue, based on combinatorial cross-reactive receptors and surface plasmon resonance imaging, for analysis and discrimination of different complex mixtures and bacteria. In this work, various complex mixtures of different nature such as wine, beer, and milk (either animal-based or plant-based) are used. It has been demonstrated that the electronic tongue is capable of responding differently to theses samples with good selectivity. For each of them, it can generate characteristic continuous 2D profile and 3D image, based on which the differentiation and classification of the complex mixtures have been carried out. Furthermore, it has been illustrated that the electronic tongue is efficient for monitoring the deterioration of milk. In the second part of this thesis, the electronic tongue has been applied for detection and analysis of bacteria. At first, some fluidic parameters have been optimized due to the variable morphology of these complexes and large biological objects. Under optimized experimental conditions, the electronic tongue is effective for analysis of bacteria with the possibility for quantification. Thereafter, the electronic tongue has allowed for the discrimination of different bacteria according to their genus, species and strains based on continuous 2D profiles and 3D images.L'objectif de cette thèse est d'explorer les applications potentielles d'un système de langue électronique basée sur des récepteurs combinatoires à réactivités croisées et l'imagerie par résonance de plasmons de surface, pour l'analyse et la discrimination de différents milieux complexes et de bactéries. L'étude de milieux complexes a été réalisée sur des échantillons de différentes natures comme le vin, la bière et le lait d'origines végétale et animale. Les expériences ont démontré que notre système de langue électronique est capable de répondre avec une bonne sélectivité à ces milieux complexes et qu'il génère ainsi des profils continus 2D et des images 3D, propres à chaque échantillon. La différentiation et la classification de ces divers types de boissons ont été réalisées grâce à ces signatures 2D et 3D. Le dispositif a également prouvé son efficacité pour le suivi du vieillissement du lait. Une seconde étude a été dédiée à l'application du système pour la détection de bactéries. Dans un premier temps, des paramètres fluidiques ont été optimisés comme la forme et la profondeur de la cuve ou le débit fluidique, en raison de la morphologie variable des bactéries, considérées ici comme des objets biologiques complexes et volumineux. Dans un second temps, le système s'est révélé performant pour l'analyse de bactéries et a montré la possibilité de quantifier ces analyses. En effet, la langue électronique a permis la discrimination de différentes bactéries selon leur genre, leur espèce et en fonction des souches grâce aux profils continus 2D et aux images 3D

COVID-19 and Environment: Impacts of a Global Pandemic

This is a reprint of the MDPI IJERPH Special Issue entitled "COVID-19 and Environment: Impacts of a Global Pandemic". The reprint consists of 17 papers with different topics related to the COVID-19 pandemic and environmental impacts using data from different countries all over the globe