Robust strategies for glucose control in type 1 diabetes

[EN] Type 1 diabetes mellitus is a chronic and incurable disease that affects millions of people all around the world. Its main characteristic is the destruction (totally or partially) of the beta cells of the pancreas. These cells are in charge of producing insulin, main hormone implied in the control of blood glucose. Keeping high levels of blood glucose for a long time has negative health effects, causing different kinds of complications. For that reason patients with type 1 diabetes mellitus need to receive insulin in an exogenous way. Since 1921 when insulin was first isolated to be used in humans and first glucose monitoring techniques were developed, many advances have been done in clinical treatment with insulin. Currently 2 main research lines focused on improving the quality of life of diabetic patients are opened. The first one is concentrated on the research of stem cells to replace damaged beta cells and the second one has a more technological orientation. This second line focuses on the development of new insulin analogs to allow emulating with higher fidelity the endogenous pancreas secretion, the development of new noninvasive continuous glucose monitoring systems and insulin pumps capable of administering different insulin profiles and the use of decision-support tools and telemedicine. The most important challenge the scientific community has to overcome is the development of an artificial pancreas, that is, to develop algorithms that allow an automatic control of blood glucose. The main difficulty avoiding a tight glucose control is the high variability found in glucose metabolism. This fact is especially important during meal compensation. This variability, together with the delay in subcutaneous insulin absorption and action causes controller overcorrection that leads to late hypoglycemia (the most important acute complication of insulin treatment). The proposals of this work pay special attention to overcome these difficulties. In that way interval models are used to represent the patient physiology and to be able to take into account parametric uncertainty. This type of strategy has been used in both the open loop proposal for insulin dosage and the closed loop algorithm. Moreover the idea behind the design of this last proposal is to avoid controller overcorrection to minimize hypoglycemia while adding robustness against glucose sensor failures and over/under- estimation of meal carbohydrates. The algorithms proposed have been validated both in simulation and in clinical trials.[ES] La diabetes mellitus tipo 1 es una enfermedad crónica e incurable que afecta a millones de personas en todo el mundo. Se caracteriza por una destrucción total o parcial de las células beta del páncreas. Estas células son las encargadas de producir la insulina, hormona principal en el control de glucosa en sangre. Valores altos de glucosa en la sangre mantenidos en el tiempo afectan negativamente a la salud, provocando complicaciones de diversa índole. Es por eso que los pacientes con diabetes mellitus tipo 1 necesitan recibir insulina de forma exógena. Desde que se consiguiera en 1921 aislar la insulina para poder utilizarla en clínica humana, y se empezaran a desarrollar las primeras técnicas de monitorización de glucemia, se han producido grandes avances en el tratamiento con insulina. Actualmente, las líneas de investigación que se están siguiendo en relación a la mejora de la calidad de vida de los pacientes diabéticos, tienen fundamentalmente 2 vertientes: una primera que se centra en la investigación en células madre para la reposición de las células beta y una segunda vertiente de carácter más tecnológico. Dentro de esta segunda vertiente, están abiertas varias líneas de investigación, entre las que se encuentran el desarrollo de nuevos análogos de insulina que permitan emular más fielmente la secreción endógena del páncreas, el desarrollo de monitores continuos de glucosa no invasivos, bombas de insulina capaces de administrar distintos perfiles de insulina y la inclusión de sistemas de ayuda a la decisión y telemedicina. El mayor reto al que se enfrentan los investigadores es el de conseguir desarrollar un páncreas artificial, es decir, desarrollar algoritmos que permitan disponer de un control automático de la glucosa. La principal barrera que se encuentra para conseguir un control riguroso de la glucosa es la alta variabilidad que presenta su metabolismo. Esto es especialmente significativo durante la compensación de las comidas. Esta variabilidad junto con el retraso en la absorción y actuación de la insulina administrada de forma subcutánea favorece la aparición de hipoglucemias tardías (complicación aguda más importante del tratamiento con insulina) a consecuencia de la sobreactuación del controlador. Las propuestas presentadas en este trabajo hacen especial hincapié en sobrellevar estas dificultades. Así, se utilizan modelos intervalares para representar la fisiología del paciente, y poder tener en cuenta la incertidumbre en sus parámetros. Este tipo de estrategia se ha utilizado tanto en la propuesta de dosificación automática en lazo abierto como en el algoritmo en lazo cerrado. Además la principal idea de diseño de esta última propuesta es evitar la sobreactuación del controlador evitando hipoglucemias y añadiendo robustez ante fallos en el sensor de glucosa y en la estimación de las comidas. Los algoritmos propuestos han sido validados en simulación y en clínica.[CA] La diabetis mellitus tipus 1 és una malaltia crònica i incurable que afecta milions de persones en tot el món. Es caracteritza per una destrucció total o parcial de les cèl.lules beta del pàncrees. Aquestes cèl.lules són les encarregades de produir la insulina, hormona principal en el control de glucosa en sang. Valors alts de glucosa en la sang mantinguts en el temps afecten negativament la salut, provocant complicacions de diversa índole. És per això que els pacients amb diabetis mellitus tipus 1 necessiten rebre insulina de forma exògena. Des que s'aconseguís en 1921 aïllar la insulina per a poder utilitzar-la en clínica humana, i es començaren a desenrotllar les primeres tècniques de monitorització de glucèmia, s'han produït grans avanços en el tractament amb insulina. Actualment, les línies d'investigació que s'estan seguint en relació a la millora de la qualitat de vida dels pacients diabètics, tenen fonamentalment 2 vessants: un primer que es centra en la investigació de cèl.lules mare per a la reposició de les cèl.lules beta i un segon vessant de caràcter més tecnològic. Dins d' aquest segon vessant, estan obertes diverses línies d'investigació, entre les que es troben el desenrotllament de nous anàlegs d'insulina que permeten emular més fidelment la secreció del pàncrees, el desenrotllament de monitors continus de glucosa no invasius, bombes d'insulina capaces d'administrar distints perfils d'insulina i la inclusió de sistemes d'ajuda a la decisió i telemedicina. El major repte al què s'enfronten els investigadors és el d'aconseguir desenrotllar un pàncrees artificial, és a dir, desenrotllar algoritmes que permeten disposar d'un control automàtic de la glucosa. La principal barrera que es troba per a aconseguir un control rigorós de la glucosa és l'alta variabilitat que presenta el seu metabolisme. Açò és especialment significatiu durant la compensació dels menjars. Aquesta variabilitat junt amb el retard en l'absorció i actuació de la insulina administrada de forma subcutània afavorix l'aparició d'hipoglucèmies tardanes (complicació aguda més important del tractament amb insulina) a conseqüència de la sobreactuació del controlador. Les propostes presentades en aquest treball fan especial insistència en suportar aquestes dificultats. Així, s'utilitzen models intervalares per a representar la fisiologia del pacient, i poder tindre en compte la incertesa en els seus paràmetres. Aquest tipus d'estratègia s'ha utilitzat tant en la proposta de dosificació automàtica en llaç obert com en l' algoritme en llaç tancat. A més, la principal idea de disseny d'aquesta última proposta és evitar la sobreactuació del controlador evitant hipoglucèmies i afegint robustesa.Revert Tomás, A. (2015). Robust strategies for glucose control in type 1 diabetes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56001TESI

Knowledge-based incremental induction of clinical algorithms

The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals

Analysis of Interval Censored Data Using a Longitudinal Biomarker

In many medical studies, interest focuses on studying the effects of potential risk factors on some disease events, where the occurrence time of disease events may be defined in terms of the behavior of a biomarker. For example, in diabetic studies, diabetes is defined in terms of fasting plasma glucose being 126 mg/dl or higher. In practice, several issues complicate determining the exact time-to-disease occurrence. First, due to discrete study follow-up times, the exact time when a biomarker crosses a given threshold is unobservable, yielding so-called interval censored events. Second, most biomarker values are subject to measurement error due to imperfect technologies, so the observed biomarker values may not reflect the actual underlying biomarker levels. Third, using a common threshold for defining a disease event may not be appropriate due to patient heterogeneity. Finally, informative diagnosis and subsequent treatment outside of observational studies may alter observations after the diagnosis. It is well known that the complete case analysis excluding the externally diagnosed subjects can be biased when diagnosis does not occur completely at random. To resolve these four issues, we consider a semiparametric model for analyzing threshold-dependent time-to-event defined by extreme-value-distributed biomarkers. First, we propose a semiparametric marginal model based on a generalized extreme value distribution. By assuming the latent error-free biomarkers to be non-decreasing, the proposed model implies a class of proportional hazards models for the time-to-event defined for any given threshold value. Second, we extend the marginal likelihood to a pseudo-likelihood by multiplying the likelihoods over all observation times. Finally, to adjust for externally diagnosed cases, we consider a weighted pseudo-likelihood estimator by incorporating inverse probability weights into the pseudo-likelihood by assuming that external diagnosis depends on observed data rather than unobserved data. We estimate the three model parameters using the nonparametric EM, pseudo-EM and weighted-pseudo-EM algorithm, respectively. Herein, we theoretically investigate the models and estimation methods. We provide a series of simulations, to test each model and estimation method, comparing them against alternatives. Consistency, convergence rates, and asymptotic distributions of estimators are investigated using empirical process techniques. To show a practical implementation, we use each model to investigate data from the ARIC study and the diabetes ancillary study of the ARIC study.Doctor of Philosoph

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This thesis discusses six scientific works within the fields of systems biology and bioinformatics. These works are unified in their conception of the cell as a system of integrated fluxes of mass and information, in the application of computational approaches to answer the questions at hand and in their aim for computation to drive new biological discoveries. The overarching theme is that by bringing computational methodologies in contact with quantitative experimental data, new principles can be proposed and/or tested that would not otherwise have been discovered. The first three chapters of this thesis focus on (the cell cycle of) budding yeast. Specifically, Ch. 2 deals with kinetic models for waves-of-cyclins. Ch. 3 concerns analysis of ChIP-exo experiments to retrieve the specific binding sites at gene promoters where Forkhead transcription factors Fkh1 and Fkh2 bind. Ch. 4 presents a web-based database and visualization tool which integrates a variety of sources of information concerning all protein-coding genes and allows users to craft specific and visualizations of the topology of interaction networks. The last three chapters of this thesis focus on that other process by which life produces more of itself: metabolism. Specifically, our focus is on thermodynamics and metabolic networks in acetogenic bacteria (Ch. 5) and human liver (Ch. 6-7). Ch. 5 is concerned with the concept of gear-shifting: an organism's hypothetical ability to express metabolic enzymes that result in different stoichiometric yields in order to navigate a trade-off between rate and yield. In Ch. 6 and 7 we discuss two approaches to deal partially with concentrations in flux balance analysis, i.e. in terms of serum concentrations of biomarkers (Ch. 6) and in terms of uptake fluxes and concentrations of medium metabolites and metabolic enzymes (Ch. 7). The six chapters present new datasets, provide novel tools, develop new models, propose novel (extensions of) computational methodologies and rationalize and assess existing methodologies. As such, this thesis provides a glance into the cutting-edge of biomedical research in this data-driven, computation-assisted age

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds