467 research outputs found

    Machine learning techniques to forecast non-linear trends in smart environments

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model

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    Type 2 diabetes mellitus (T2DM) refers to the inability to produce or respond to insulin, resulting in an elevated blood glucose level in the human body. Due to concerns over current diabetes screening and diagnostic procedures that require fasting, oral glucose consumption, and invasive nature (finger prick), the number of undiagnosed T2DM increases yearly. The increase is due to the hesitation of individuals to undergo screening tests as their routine check-up. As T2DM is closely related to blood glucose levels, a predictive model is developed to predict blood glucose levels, which can be used as an alternative for screening T2DM. Thus, the present study proposed a multiple linear regression equation for predicting the fasting blood glucose level based on independent parameters of lipid profile and blood pressure. It is widely known that high blood cholesterol and high blood pressure are the risk factors of T2DM. In this study, a set of 302 data was collected from UMP's retrospective data via the data directory of the University Health Centre from 2017 to 2018. The present study used 211 (70%) data to fit the predictive model, whereas another 91 (30%) of the data were used for selfvalidation of the model. Moreover, the overall model performance was observed by refitting the whole data set (n = 302, 100%) into the predictive model equation. The main outcome of the study showed that 46.8% (adjusted R2= 0.468, p-value < 0.05) of the fasting blood glucose level could be predicted using multiple linear regression based on high-density lipoprotein cholesterol, triglycerides, and systolic blood pressure levels without the standard fasting procedure. The prediction made by this model is acceptable with moderate accuracy (MAPE = 9.46%). This predictive model is easily adaptable to data changes (the difference of error metric values between the training data and testing data: MAE = 0.1836 mmol/L, RMSE = 0.1040 mmol/L, and MAPE = 3.93%). Thus, in order to increase the accuracy of the model, future research should consider a bigger and broader cohort from different comorbidities, which can be an alternative method in screening T2DM

    Intrinsic connectivity changes mediate the beneficial effect of cardiovascular exercise on sustained visual attention

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    Cardiovascular exercise (CE) is an evidence-based healthy lifestyle strategy. Yet, little is known about its effects on brain and cognition in young adults. Furthermore, evidence supporting a causal path linking CE to human cognitive performance via neuroplasticity is currently lacking. To understand the brain networks that mediate the CE-cognition relationship, we conducted a longitudinal, controlled trial with healthy human participants to compare the effects of a 2-week CE intervention against a non-CE control group on cognitive performance. Concomitantly, we used structural and functional magnetic resonance imaging to investigate the neural mechanisms mediating between CE and cognition. On the behavioral level, we found that CE improved sustained attention, but not processing speed or short-term memory. Using graph theoretical measures and statistical mediation analysis, we found that a localized increase in eigenvector centrality in the left middle frontal gyrus, probably reflecting changes within an attention-related network, conveyed the effect of CE on cognition. Finally, we found CE-induced changes in white matter microstructure that correlated with intrinsic connectivity changes (intermodal correlation). These results suggest that CE is a promising intervention strategy to improve sustained attention via brain plasticity in young, healthy adults

    Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

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    Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

    Metabolic dysfunction-associated steatotic liver disease:A wide-angled perspective on a multifaceted problem

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    In dit proefschrift is metabole dysfunctie-geassocieerde steatotische leverziekte (MASLD) onderzocht, opgedeeld in drie delen:Deel 1 omvat een MRI-onderzoek naar vetstapeling in de lever en pancreas, en een genoombrede associatiestudie in de Amsterdamse multi-etnische populatie, waarbij een relatie tussen het gen MRC1 en niet-invasieve leverfibrosetesten wordt gevonden. Dit gen vertoont variaties tussen etnische groepen, wat wijst op een rol van MRC1 in de bestaande verschillen in MASLD tussen bevolkingsgroepen van verschillende afkomst.In deel 2 is gericht op nieuwe niet-invasieve levertesten van fibrosevorming in mensen met MASLD. Ten eerste een systematische review van de marker Pro-C3 voor het detecteren van fibrose, en ten tweede een onderzoek naar een nieuw niet-invasief biomarkerpanel, van ontdekking in muisstudies tot bevestiging in humane cohorten.Deel 3 van het proefschrift beschrijft de potentie van het darmmicrobioom om MASLD te beïnvloeden. Een fecestransplantatiestudie toont aan dat het manipuleren van het darmmicrobioom leidt tot veranderingen in circulerende metabolieten en lever-DNA-methylatie. Daarnaast beschrijft een muisonderzoek het effect van de boterzuurproducerende bacterie A. soehngenii op de ernst van MASLD, waarbij toediening ervan de suikerhuishouding verbeterde zonder verbetering van de leverhistologie.Gezamenlijk leveren de studies die in dit proefschrift beschreven zijn waardevolle inzichten in de complexiteit van MASLD en bieden ze verschillende potentiële mogelijkheden om de zorg voor mensen met MASLD te verbeteren door middel van genetische, metabole en microbioom-gerichte benaderingen

    Amelioration of Mitochondrial Bioenergetic Dysfunction in Diabetes Mellitus: Delving into Specialized and Non-specific Therapeutics for the Ailing Heart

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    Morbidity and mortality of the diabetic population is influenced by many confounding factors, but cardiovascular disease (CVD), remains the leading cause of death. Mitochondrial dysfunction is central in the development of cardiac contractile dysfunction, with decreased mitochondrial bioenergetic function, increased dependence on free fatty acid utilization, and a decrease in glucose utilization having been shown to contribute to contractile dysfunction. Strategies targeting the amelioration of mitochondrial bioenergetic function are attractive for limiting diabetes-induced heart failure, and preserving health-span. The goals of this dissertation were to assess two mitochondrial-centric approaches for the amelioration of mitochondrial and cardiac contractile dysfunction in diabetes mellitus. Our laboratory previously identified microRNA-378a (miR-378a) as a regulator of mitochondrially encoded ATP synthase membrane subunit 6 (mt-ATP6) mRNA, a component of the ATP synthase F0 complex. More recently, a second class of non-coding RNAs, long non-coding RNAs (lncRNA), have been proposed to regulate microRNA activity. LncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), is predicted to bind miR-378a. Chapter 2 aimed to determine if inhibition of miR-378a could ameliorate cardiac contractile dysfunction in type 2 diabetes mellitus (T2DM), and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a genomic loss, and inhibition by Kcnq1ot1, improved ATP synthase functionality, and preserved cardiac contractile function. Together, Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content. By acting as therapeutic targets, their manipulation may provide benefit to ATP synthase functionality in the heart during T2DM. A second method of ameliorating mitochondrial dysfunction is mitochondrial transplantation. Current literature suggests that mitochondrial transplantation may be of benefit to the diabetic heart. Chapter 3 aimed to assess mitochondrial transplantation as a prophylactic method of treating mitochondrial dysfunction in the diabetic heart. Following mitochondrial transplantation in vivo using ultrasound-guided echocardiography, mitochondrial signal was detectable in at least 30% of the left ventricle myocardium, primarily within and near injection sites. Poor mitochondrial distribution indicated a need for a more focused injection strategy aimed at targeting a cardiac region or segment of interest. Speckle tracking echocardiography has been utilized to evaluate spatial and progressive alterations in the diabetic heart independently, but the spatial and temporal manifestation of cardiac dysfunction remain elusive. Therefore, the objectives of Chapter 4 were to elucidate if cardiac dysfunction associated with T2DM occurs spatially, and if patterns of regional or segmental dysfunction manifest in a temporal fashion. Non-invasive echocardiography datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. Machine learning was used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. Overall, the Septal region, and the AntSeptum segment, best represented cardiac dysfunction associated with the diabetic state at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. These results suggested that cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the diabetic heart. Further, the Septal region, and AntSeptum segment, may provide a locale of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM

    Non-Invasive Continuous Glucose Monitoring: Identification of Models for Multi-Sensor Systems

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    Diabetes is a disease that undermines the normal regulation of glucose levels in the blood. In people with diabetes, the body does not secrete insulin (Type 1 diabetes) or derangements occur in both insulin secretion and action (Type 2 diabetes). In spite of the therapy, which is mainly based on controlled regimens of insulin and drug administration, diet, and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day, blood glucose concentration often exceeds the normal range thresholds of 70-180 mg/dL. While hyperglycaemia mostly affects long-term complications (such as neuropathy, retinopathy, cardiovascular, and heart diseases), hypoglycaemia can be very dangerous in the short-term and, in the worst-case scenario, may bring the patient into hypoglycaemic coma. New scenarios in diabetes treatment have been opened in the last 15 years, when continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration continuously (i.e. with a reading every 1 to 5 min) over several days, entered clinical research. CGM sensors can be used both retrospectively, e.g., to optimize the metabolic control, and in real-time applications, e.g., in the "smart" CGM sensors, able to generate alerts when glucose concentrations are predicted to exceed the normal range thresholds or in the so-called "artificial pancreas". Most CGM sensors exploit needles and are thus invasive, although minimally. In order to improve patients comfort, Non-Invasive Continuous Glucose Monitoring (NI-CGM) technologies have been widely investigated in the last years and their ability to monitor glucose changes in the human body has been demonstrated under highly controlled (e.g. in-clinic) conditions. As soon as these conditions become less favourable (e.g. in daily-life use) several problems have been experienced that can be associated with physiological and environmental perturbations. To tackle this issue, the multisensor concept received greater attention in the last few years. A multisensor consists in the embedding of sensors of different nature within the same device, allowing the measurement of endogenous (glucose, skin perfusion, sweating, movement, etc.) as well as exogenous (temperature, humidity, etc.) factors. The main glucose related signals and those measuring specific detrimental processes have to be combined through a suitable mathematical model with the final goal of estimating glucose non-invasively. White-box models, where differential equations are used to describe the internal behavior of the system, can be rarely considered to combine multisensor measurements because a physical/mechanistic model linking multisensor data to glucose is not easily available. A more viable approach considers black-box models, which do not describe the internal mechanisms of the system under study, but rather depict how the inputs (channels from the non-invasive device) determine the output (estimated glucose values) through a transfer function (which we restrict to the class of multivariate linear models). Unfortunately, numerical problems usually arise in the identication of model parameters, since the multisensor channels are highly correlated (especially for spectroscopy based devices) and for the potentially high dimension of the measurement space. The aim of the thesis is to investigate and evaluate different techniques usable for the identication of the multivariate linear regression models parameters linking multisensor data and glucose. In particular, the following methods are considered: Ordinary Least Squares (OLS); Partial Least Squares (PLS); the Least Absolute Shrinkage and Selection Operator (LASSO) based on l1 norm regularization; Ridge regression based on l2 norm regularization; Elastic Net (EN), based on the combination of the two previous norms. As a case study, we consider data from the Multisensor device mainly based on dielectric and optical sensors developed by Solianis Monitoring AG (Zurich, Switzerland) which partially sponsored the PhD scholarship. Solianis Monitoring AG IP portfolio is now held by Biovotion AG (Zurich, Switzerland). Forty-five recording sessions provided by Solianis Monitoring AG and collected in 6 diabetic human beings undertaken hypo and hyperglycaemic protocols performed at the University Hospital Zurich are considered. The models identified with the aforementioned techniques using a data subset are then assessed against an independent test data subset. Results show that methods controlling complexity outperform OLS during model test. In general, regularization techniques outperform PLS, especially those embedding the l1 norm (LASSO end EN), because they set many channel weights to zero thus resulting more robust to occasional spikes occurring in the Multisensor channels. In particular, the EN model results the best one, sharing both the properties of sparseness and the grouping effect induced by the l1 and l2 norms respectively. In general, results indicate that, although the performance, in terms of overall accuracy, is not yet comparable with that of SMBG enzyme-based needle sensors, the Multisensor platform combined with the Elastic-Net (EN) models is a valid tool for the real-time monitoring of glycaemic trends. An effective application concerns the complement of sparse SMBG measures with glucose trend information within the recently developed concept of dynamic risk for the correct judgment of dangerous events such as hypoglycaemia. The body of the thesis is organized into three main parts: Part I (including Chapters 1 to 4), first gives an introduction of the diabetes disease and of the current technologies for NI-CGM (including the Multisensor device by Solianis) and then states the aims of the thesis; Part II (which includes Chapters 5 to 9), first describes some of the issues to be faced in high dimensional regression problems, and then presents OLS, PLS, LASSO, Ridge and EN using a tutorial example to highlight their advantages and drawbacks; Finally, Part III (including Chapters 10-12), presents the case study with the data set and results. Some concluding remarks and possible future developments end the thesis. In particular, a Monte Carlo procedure to evaluate robustness of the calibration procedure for the Solianis Multisensor device is proposed, together with a new cost function to be used for identifying models

    Data Mining

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    Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

    Medical Informatics and Data Analysis

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    During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role
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