Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others

Standardizing the Collection and Measurement of Glucose in Exhaled Breath and Its Relationship to Blood Glucose Concentrations

Blood glucose level control (glycemic control) is crucial in diabetes. Limitations in current commercially available monitoring devices include causing patient pain leading to poor blood glucose level management. The development of a non-invasive measurement system may lead to improved patient glycemic control, reducing unwanted side-effects and complications of poor blood glucose level maintenance. This work explores the use of glucose within exhaled breath in attempt to establish an indirect method of blood glucose level measurement. Specifically, exhaled breath condensate (EBC) is examined. A breath condensing unit was designed to measure the temperature of the system, flow rate, volume of expired air, ambient humidity, and remove exhaled dead volume before condensing breath. A fluorometric assay was used to analyze and measure the glucose concentrations in the EBC samples. The results directly relate to the feasibility of developing a noninvasive EBC-based glucose measuring device. A nebulizer study was performed to verify that the amount of glucose present in the condensate was predictable, given a known concentration of aerosolized glucose. The nebulizer study revealed that some glucose interferent is present in the ambient air. Further exploration allowed for a humidity based model to be developed that can accurately and consistently predict the concentration of the condensate. An IRB approved study, using a total of five human subjects, was employed to quantitatively evaluate the change in both blood and EBC glucose levels associated with the intake of either food or water. The human subject study results indicate that, with the use of the humidity based model derived from the nebulizer study, it is possible to predict blood glucose levels from EBC glucose levels. These results provide motivation for the further exploration of an EBC-based non-invasive blood monitoring device

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

Boundaries in volatile organic compounds in human breath

Exhaled breath is a rich and complex matrix containing many hundreds of compounds. Every breath offers the potential of a non-invasive measurement of the biochemical processes occurring in the human body and it is this notion that has led to the application of breath analysis for the detection of disease. With the majority of research in the field being focused on the detection of biomarkers, little has been presented on how the seemingly homeostatic matrix of breath varies during the course of normal life events. The research in this thesis describes how a subject’s emotional state, physical state, and daily activities can alter the composition of exhaled breath. [Continues.

Detection of volatile organic compounds in the alveolar air of subjects with stress-related psychopathologies

Dosaggio dei composti organici volatili nell\u2019aria alveolare di soggetti con psicopatologie stress-correlate L\u2019aumento della domanda lavorativa insieme a maggiori livelli di stress hanno effetti negative sulla salute dei lavoratori, compresa l\u2019insorgenza di disturbi mentali. Lo stress \ue8 all\u2019origine di diverse forme di disagio lavorativo tra cui il Mobbing, la Costrittivit\ue0 organizzativa e il Burn-out che a loro volta sono espressione di stress psicosociale. Molti studi includono tra le psicopatologie stress-correlate il disturbo misto ansioso-depressivo, il disturbo d\u2019ansia e il disturbo depressivo. L\u2019INAIL riconosce ed indennizza il Disturbo dell\u2019Adattamento e il Disturbo Post-Traumatico da Stress come malattie professionali riconducibili a situazioni di Costrittivit\ue0 organizzativa. La fisiopatologia dello stress \ue8 nota da tempo e i biomarcatori attualmente utilizzati per la diagnosi dello stress, come il cortisolo in matrici biologiche quali sangue, urina e saliva si ottengono con metodi invasivi e protocolli complessi. Nell\u2019ultimo decennio sono stati pubblicati molti studi che hanno documentato il possibile ruolo dell\u2019aria alveolare nella diagnosi di diverse patologie (neoplastiche, metaboliche, infiammatorie) fornendo informazioni riguardo lo stato metabolico del paziente. Alcuni studi hanno applicato l\u2019analisi dell\u2019aria alveolare anche alle psicopatologie, in particolare la schizofrenia. 98 lavoratori con disturbi mentali e somatoformi correlati a condizioni di stress lavorativo cronico e 80 volontari sani sono stati sottoposti alla raccolta di campioni di aria alveolare. Sono state dosate le concentrazioni di circa cento composti organici volatili nell\u2019espirio utilizzando l\u2019analisi in spettrometria di massa \u2013 IMR. La maggior parte dei composti era nota solo per la massa. I dati sono stati analizzati mediante analisi statistica multivariata; si sono ottenuti due modelli che con una combinazione rispettivamente di undici molecole (pentano, ciclopentadiene, acetonitrile, butadiene, acido cianidrico, M70, M71, M74, M75, M97 E M123) e di tre molecole (M27, eptano e M 101) sono stati in grado distinguere i casi dai controlli sani con una sensibilit\ue0 del 100% e una specificit\ue0 del 98,75%. L\u2019utilizzo della spettrometria di massa ha comportato numerosi vantaggi: innanzitutto il breve tempo richiesto per dosare circa cento molecole per ciascun campione e il costo contenuto grazie alla possibilit\ue0 di dosare simultaneamente centinaia di campioni al giorno; inoltre l\u2019elevata sensibilit\ue0 ha permesso di quantificare molecole a concentrazioni bassissime (ppb: parti per bilione). Il prelievo di aria alveolare \ue8 semplice, non invasivo e l\u2019analisi strumentale non \ue8 costosa. L\u2019identificazione di uno o pi\uf9 profili di composti organici volatili elettivi nell\u2019aria alveolare potrebbe essere utile nella diagnosi delle psicopatologie stress correlate.Detection of volatile organic compounds in the alveolar air of subjects with stress-related psychopathologies The upsurge of job demand together with more high-level of Stress, have negative effects on the health of workers including mental disorders. Stress is responsible for various forms of working uneasiness, among which Personal Bullying, Task-related Bullying and Burn-Out that are different expressions of psychosocial Stress. Manifold studies include Depression, Anxious and mixed Anxious-Depressive Disorders among the stress-related psychopathologies. The Italian Institute for Insurance against Industrial Accident (INAIL) recognizes and indemnifies the Post-Traumatic Stress Disorder (PTSD) and the Adjustment Disorder as professional illnesses referable to situations of Task-related Bullying. The physiopathology of stress has been known for many years and the biomarkers used for the diagnosis of stress such as cortisol in biological matrices like blood, urine and saliva are obtained with invasive methods and nonelementary protocols. In the last decade many studies have been published documenting the potential role of the alveolar air in the diagnosis of different pathologies (neoplastic, metabolic, inflammatory) through informations on the metabolic state of the patient. Some studies have also applied the analysis of the alveolar air to the psychopathologies, mainly to schizophrenia. 98 workers with mental and somatoform disorders correlated with situations of chronic stress at work and 80 healthy volunteers were submitted to the alveolar air sampling. Concentrations of about one hundred volatile compounds were measured in the exhaled breath using IMR - Mass Spectrometry. Most of the compounds were known only for their masses. A multivariate statistical analysis was performed. Using two different models with a combination respectively of eleven molecules (pentane, cyclopentadiene, acetonitrile, butadiene, hydrocyanic acid, M70, M71, M74, M75, M97 and M123) and three molecules (M27, heptane and M101) has been possible to distinguish the cases from the healthy controls with a 100% sensibility and a 98,75% specificity. The use of mass spectrometry has entailed different advantages: first of all the short time required for the dosing of about one hundred molecules in each sample and the low cost thanks to the possibility to simultaneously analyze hundreds of samples a day; besides the high sensibility of the mass spectrometry analysis allows to quantify molecules at very low concentrations (ppb). Alveolar air sampling is simple and non-invasive and the instrumental analysis is not expensive. The identification of one or more profiles of elective volatile organic compounds in the alveolar air would be useful in the diagnosis of stress-related psychopathologies

Characterization of human expired breath by solid phase microextraction and analysis using gas chromatography-mass spectrometry and differential mobility spectrometry

Thesis (M. Eng.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 92-95).Breath analysis has potential to become a new medical diagnostic modality. In this thesis, a method for the analysis of human expired breath was developed using gas chromatography-mass spectroscopy. It was subsequently adopted for gas chromatography-differential mobility spectroscopy, a modality not previously applied to this problem. Tedlar bags and solid-phase microextraction were used for breath sampling and concentration prior to analysis. Four fiber coatings were evaluated with respect to selectivity and sensitivity; extraction time, gas chromatography temperature programming, and sample storage stability were explored for optimization. The method entails extraction and preconcentration with a polydimethylsiloxane-divinylbenzene coated fiber for 30 min at 37⁰C, and extraction profiles for several compounds demonstrate competitive adsorption. 120 compounds were identified in breath with response variability between 23 - 117% about mean values. Feasibility of differential mobility spectroscopy for breath analysis was established, and this method will be the basis for future investigations on the diagnostic potential of breath analysis.by William Merrick.M.Eng

Exhaled Breath Analysis for Non-Invasive Diagnosis of Tropical Diseases

Les malalties tropicals desateses (MTD) pertanyen al grup de malalties infeccioses. Són endèmiques en la major part del món i afecten més de mil milions de persones a tot el món, especialment a les poblacions de baixos ingressos de les regions en desenvolupament. La infecció en humans es caracteritza per un període d'incubació asimptomàtica crònica i perllongada sense símptomes notables de la malaltia, el que retarda la prescripció d'un tractament mèdic adequat i oportú. La prevenció, el diagnòstic i el control d'aquestes malalties segueixen sent un desafiament metge no resolt encara. Aquesta tesi ha tingut com a objectiu desenvolupar una metodologia no invasiva, segura i amigable per al pacient per a un diagnòstic ràpid de les MTD. L'enfocament de la meva tesi es va basar en el diagnòstic de la malaltia a través de l'anàlisi de mostres d'alè exhalat, que són fàcils d'obtenir i no presenten molèsties ni riscos per a la salut dels pacients. El treball de la meva tesi es va centrar en tres tipus diferents de malalties tropicals desateses (dengue, equinococcosi i leishmaniosi) causades per tres patògens diferents (infeccions virals, helmínticas i protozoàries, respectivament). Les mostres d'alè es van recollir amb dispositius homologats Bio-VOC, que són fàcils d'usar i requereixen d'una preparació mínima. Per analitzar les mostres d'alè, primer es van emprar tècniques analítiques estàndard per a la identificació dels biomarcadors volàtils d'aquestes malalties. Un altre dels objectius de la meva tesi va ser dissenyar i fabricar una matriu de sensors químics de gasos amb sensibilitats creuades basats en nanopartícules de metall ultrapur funcionalitzades amb diversos compostos orgànics, per la qual cosa vaig emprar una tècnica innovadora basada en deposició física en fase vapor. Els nous sensors van ser caracteritzats i es van emprar per identificar els patrons de les malalties estudiades en les mostres d'alè. Les respostes dels sensors químics de gasos exposats a les mostres d'alè es van usar per a construir models de reconeixement de patrons per al diagnòstic d'aquestes malalties. Els resultats obtinguts han revelat que l'anàlisi de l'alè exhalat amb una matriu de sensors de gasos basats en nanoensamblajes de nanopartícules de metall ultrapur té un gran potencial com a prova diagnòstica fiable per a les MTD.Las enfermedades tropicales desatendidas (ETD) pertenecen al grupo de enfermedades infecciosas. Son endémicas en la mayor parte del mundo y afectan a más de mil millones de personas en todo el mundo, especialmente a las poblaciones de bajos ingresos de las regiones en desarrollo. La infección en humanos se caracteriza por un período de incubación asintomática crónica y prolongada sin síntomas notables de la enfermedad, lo que retrasa la prescripción de un tratamiento médico adecuado y oportuno. La prevención, el diagnóstico y el control de estas enfermedades siguen siendo un desafío médico no resuelto aún. Esta tesis ha tenido como objetivo desarrollar una metodología no invasiva, segura y amigable para el paciente para un diagnóstico rápido de las ETD. El enfoque de mi tesis se basó en el diagnóstico de la enfermedad a través del análisis de muestras de aliento exhalado, que son fáciles de obtener y no presentan molestias ni riesgos para la salud de los pacientes. El trabajo de mi tesis se centró en tres tipos diferentes de enfermedades tropicales desatendidas (dengue, equinococosis y leishmaniasis) causadas por tres patógenos diferentes (infecciones virales, helmínticas y protozoarias, respectivamente). Las muestras de aliento se recogieron con dispositivos homologados Bio-VOC, que son fáciles de usar y requieren de una preparación mínima. Para analizar las muestras de aliento, primero se emplearon técnicas analíticas estándar para la identificación de los biomarcadores volátiles de estas enfermedades. Otro de los objetivos de mi tesis fue diseñar y fabricar una matriz de sensores químicos de gases con sensibilidades cruzadas basados en nanopartículas de metal ultrapuro funcionalizadas con diversos compuestos orgánicos, para lo cual empleé una técnica innovadora basada en deposición física en fase vapor. Los nuevos sensores fueron caracterizados y se emplearon para identificar los patrones de las enfermedades estudiadas en las muestras de aliento. Las respuestas de los sensores químicos de gases expuestos a las muestras de aliento se usaron para construir modelos de reconocimiento de patrones para el diagnóstico de estas enfermedades. Los resultados obtenidos han revelado que el análisis del aliento exhalado con una matriz de sensores de gases basados en nanoensamblajes de nanopartículas de metal ultrapurNeglected Tropical Diseases (NTDs) belong to the group of infectious diseases. They are endemic in most parts of the world, affecting more than one billion people worldwide, especially low income populations from developing regions. The infection to humans is characterized by a chronic and prolonged asymptomatic incubation period without noticeable symptoms of the disease, which delays the prescription of a suitable and timely medical treatment. The prevention, diagnosis and control of these diseases still remain an unsolved medical challenge. This thesis aimed to develop a non-invasive, safe and patient-friendly methodology for rapid diagnosis of NTDs. The thesis approach was based on disease diagnosis via exhaled breath samples analyses, which are easy to obtain and present no discomfort or risk for patients’ health. The thesis work was focused on three different types of neglected tropical diseases (Dengue, Echinococcosis and Leishmaniasis) caused by three different pathogens (viral, helminthic and protozoan infections, respectively). Breath collection was realized with homologated Bio-VOCTM breath samplers, which are simple and user friendly and require minimal training. For analyzing the breath samples, at first standard analytical techniques were employed for the identification of the breath volatile biomarkers of these diseases. As another objective of my thesis, an array of cross reactive chemical gas sensors based on ultrapure metal nanoparticles – ligand nanoassemblies comprising diverse functional organic ligands was designed and fabricated employing an innovative physical deposition route. The new sensors were characterized and employed for the analysis of the breath print profiles of the diseases under study. The responses of the chemical gas sensors to the breath samples were used to build predictive pattern recognition models for the diagnoses of these diseases. The results obtained revealed that exhaled breath analysis with cross reactive gas sensors arrays based on ultrapure metal nanoparticles-ligand nanoassemblies holds significant potential as a cost-effective, simple and non-invasive diagnostic test for NTDs

Breath analysis : methodology towards a fieldable breath analysis device

In this work lung cancer is introduced along with the current detection methods. The inadequacies of the current situation are highlighted along with the need for better detection technologies that would allow for a more rigorous testing regime to be implemented. Metabolism and metabolites are introduced as potential biomarkers. The advanced detection techniques mass spectrometry (MS) and differential mobility spectrometry (DMS) are introduced and discussed with regard to being a fieldable device. The methods applicable to processing data generated by these instruments are discussed. Finally the research objectives are highlighted. The science of breath sampling is discussed along with the considerations when engaging in breath analysis research. Sampling and trapping of volatile organic compounds (VOCs) is discussed with particular emphasis on the adaptive breath sampler which was used in this work. The benefits of a dual detector instrument allowing for analysis of a single sample using both MS and DMS are outlined. The design and implementation of a parallel, two detector system is outlined including the intricacies of balancing the two columns that operate at different pressures and developing a mount Processing DMS data currently lags behind the current hardware available as there are no methods that allow the full data surface to be utilised. This work outlines a method for transforming DMS data from three dimensions to two dimensions while retaining the full information contained within the data surface. This method was tested with generated data sets to show its' utility and compared to the current standard processing method using real data sets. An understanding of all aspects of a clinical research project is vital to ensure the smooth running and completion of the project. The currently required documentation for an outside researcher to work within the NHS are detailed along with the expected timeframe for each step of designing, gaining ethical approval and implementing the research. The use of Gantt charts and work flow diagrams is highlighted and examples are given. An initial inspection of the data produced by a pilot study shows that there a several challenges that must be overcome, these are contamination and artefact peaks, retention time shifting, unresolved peaks, differing intensities in similar samples and the complexities of correctly identifying compounds found in breath samples. These are discussed and a workflow is highlighted.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using the Concept of Complexity to Guide Translational Research in Psychiatry

The presented work unites several distinct lines of research, asserting that the broader construct of complexity (with its various connotations) may be uniquely relevant and informative to our understanding and management of psychotic disorders. Part 1 outlines the candidate’s contributions to the development of EEG methodology in clinical populations in an effort to most directly capture neural noise and complexity. The advent of oscillatory analysis facilitated the study of ongoing background activity of the EEG. Further exploration of this background activity demonstrated that increased neural noise (as quantified by Lempel-Ziv complexity) is highly correlated with, and conceptually very relevant to, positive symptoms of psychosis. Part 2 describes how considering the complexity of clinical psychosis states justifies the use of human laboratory studies using psychotomimetic drugs such as tetrahydrocannabinol and ketamine. Part 3 explains how the ideas and inferences from the work in Parts 1 and 2 can inform the environment of psychotic disorders, specifically the candidate’s work in prescribing practices and first episode psychosis service design. The thesis concludes that EEG studies of clinical populations face particular methodological challenges, however the resultant technical advancements have expanded our view of neural function to the particular benefit of our understanding of psychosis. EEG measures of complexity may be amongst the most sensitive biomarkers associated with positive symptoms, however more empirical research is called for to confirm this observation. Human laboratory studies of psychotomimetic drugs in healthy humans may continue to prove useful, in circumventing the phenomenological and patho-etiological complexity of clinically occurring psychosis. As a next step, multi-modal studies (combining biophysical signals, individual phenomenology and even population level outcomes) in combination with data mining techniques might further characterize the complexity within psychosis. Psychotic disorders, as complex problems, warrant framing and intervention informed by complexity