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Objectives Melody TPV is the first percutaneous valvular bio–prosthesis approved for trans–catheter pulmonary valve implantation (TPVI). We describe our ten–years experience about Melody TPV implantation in patients with congenital heart disease (CHD). Methods This is an observational retrospective single–center study. All patients undergone Melody TPV implantation were included. The early outcomes analyzed were: procedural failure, death of patient, life–threating adverse events. The long–term outcomes analyzed during follow–up were: infective endocarditis, trans–catheter redo–procedure, and surgical redo–procedure. Results From 2012 to 2022, 39 patients were evaluated in our catheterization laboratory for TPVI with Melody TPV. Mean age and weight were 21,31±11,58 (range 8–66) years and 57,5±16,24 (range 23–96) kilograms. The patients were affected by the following CHD: tetralogy of Fallot (22 pts, 56,4%), pulmonary atresia with ventricular septal defect (VSD) (4 pts, 10,3%), aortic stenosis s/p Ross surgery (4 pts, 10,3%), transposition of the great arteries with VSD and PS (3 pts, 7,7%), pulmonary stenosis (PS) (2 pts, 5,1%), truncus arteriosus (2 pts, 5,1%), double outlet right ventricle (1 pt, 2,5%), Ebstein’s anomaly (1 pt, 2,5%) In 4 cases (10,3%), the procedure failed because of coronary compression during balloon RVOT interrogation. One patient (2,5%) died due to post–procedural septic shock. In the other 34 patients (87,2%), the Melody TPV was implanted effectively. No life–threating adverse events were recorded. In 3 cases, the Melody TPV was implanted off–label: in tricuspid position (one case) and in “small conduits” ( Conclusion Trans–catheter Melody TPV implantation is effective to deal RVOT dysfunction. At a mid–term follow–up, the most of the implanted Melody TPV worked properly and only a few patients required surgical valve replacement.

Background Transcatheter patent foramen ovale (PFO) closure is a validated strategy to prevent recurrent cerebral events in adult patients; limited data are available for children. Methods This is a retrospective analysis including pediatric population underwent to percutaneous treatment of PFO in our center. Results From 2006 to 2021, 21 transcatheter PFO closures have been performed in children (mean age: 14 +3 years; female: 58%, weight: 59 + 20 kg). Indications for the closure of PFO consisted in the occurrence of cryptogenic ischemic events for 18 patients (86%), in disabling migraine symptoms for 2 patients (9,5%) and in prophylactic measure to prevent paradoxical embolism in high risk abdominal surgery for 1 patient (4,5%). Transcatheter PFO closure was performed under general anesthesia with transesophageal ultrasound guidance. Complex anatomies of PFO included atrial septal aneurysm in 4 cases (with accessory fenestration of atrial membrane in 2 of these cases) and long and stiff PFO tunnel in one case. Single device was used in 20 procedures. ASD closure device was required for 5 cases because of anatomic characteristics of PFO. In one case of large multifenestrated atrial septal aneurysm, percutaneous treatment was performed using both PFO closure device and ASD closure device. No peri and/or post procedural complications have been reported . All patients were treated with Acetylsalicylic acid and Clopidogrel for the first six months after the procedure. Conclusions Transcatheter PFO closure is a safe procedure in pediatric patients in high volume center that should be used for preventing recurrent cerebral events also in children.

This chapter describes the application of a recent compartment-based epidemiological model, namely the SEIAR (Susceptible-Exposed-Infectious-Asymptomatic-Recovered), to estimate the spreading of the coronavirus COVID-19 in Italy and in some of its regions. The model is here extended through the definition of a time-dependent dynamic social distancing (DSD) function, thus introducing the SEIAR–DSD model. To profitably use the SEIAR–DSD model, the most suitable values of its parameters must be found. This is performed through differential evolution, a heuristic optimization technique. This allows approximately evaluating, for each of the above-mentioned scenarios, the daily number of infectious individuals, the day(s) in which this number will reach its maximum, the corresponding value, and the future evolution of the spread.

V tomto představujeme algoritmus gramatické evoluce k odvození personalizovaných a interpretovatelných modelů předpovědi glukózy pro diabetické pacienty na základě historických měření glukózy, sacharidů a injekčně podaného inzulínu. K hodnocení odvozených modelů se používá soubor dat pacientů s diabetem 1. typu. Experimentální testy ukazují, že výkon těchto modelů je srovnatelný s modely získanými jinými nejmodernějšími technikami, které vyžadují větší výpočetní výkon. Within this paper a Grammatical Evolution algorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is comparable with that obtained by other state–of–the–art techniques that require a more significant computational effort.

SummaryBackgroundThe emerging threat represented by SARS-CoV-2 variants, demands the development of therapies for better clinical management of COVID-19. MAD0004J08 is an extremely potent Fc-engineered monoclonal antibody (mAb) able to neutralise in vitro all current SARS-CoV-2 variants of concern (VoCs). This ongoing study, evaluates safety, pharmacokinetics and SARS-CoV-2 sera neutralization effect of MAD0004J08 when administered as single dose intramuscularly in healthy adults.MethodWe conducted a dose escalation study with sequential enrolment of three cohorts, each with an increasing dose level of MAD0004J08 (48mg, 100mg and 400mg). Within each cohort, 10 young healthy adults were randomized with 4:1 ratio to a single intramuscular (i.m.) injection of MAD0004J08 or placebo. The primary endpoint is the proportion of subjects with severe and/or serious treatment emergent adverse events (TEAEs) within 7 days post-treatment. Secondary endpoints reported in this paper are the proportion of subjects with solicited TEAEs up 7 days post dosing, MAD0004J08 serum concentrations and neutralising activity versus the original SARS-COV-2 Wuhan virus at different timepoints post-dosing. As post-hoc analyses, we compared the sera neutralising titres of subjects who received MAD0004J08 with those of people that had received the COVID-19 BNT162b2 mRNA vaccine in the previous sixty days (n=10) and COVID-19 convalescent patients (n=20), and assessed serum neutralisation activity against the B.1.1.7 (alpha), B.1.351 (beta) and B.1.1.248 (gamma) SARS-CoV-2 variants of concern.FindingsA total of 30 subjects, 10 per cohort, were enrolled and randomized. Data up to 30 days were available and analysed in this report. No severe TEAEs were reported in any of the cohorts in the 7 days post-treatment. MAD0004J08 was detected in the sera of treated subjects within few hours post-administration and reached almost maximal levels on day 8. The geometric mean neutralising titres (GMT) assessed against the original Wuhan virus peaked on day 8 and ranged 226 – 905, 905 – 2,560, and 1,280 – 5,120 for cohort 1, 2 and 3 respectively. The sera neutralising GMT in MAD0004J08 treated subjects in all the three cohorts were found to be 1·5-54·5-fold higher compared to sera from convalescent patients and 1·83– 76·4-fold higher compared to sera from COVID-19 vaccinees. Finally, GMT in MAD0004J08 treated subjects showed high neutralising titres versus the B.1.1.7 (alpha), B.1.351 (beta) and B.1.1.248 (gamma) SARS-CoV-2 VoCs.InterpretationA single dose administration of MAD0004J08 via i.m. route is safe and well tolerated and results in a rapid systemic distribution of the MAD0004J08 and sera neutralising titres higher than COVID-19 convalescent and vaccinated subjects. A single dose administration of MAD0004J08 is also sufficient to effectively neutralise major SARS-CoV-2 variants of concern. Based on these results, a Phase 2-3 trial is ongoing to further assess the safety, dosage, and efficacy of MAD0004J08 in asymptomatic or mild-moderate symptomatic COVID-19 patients.FundingEU Malaria Fund, Ministero dello Sviluppo Economico, Ministero della Salute, Regione Toscana, Toscana Life Sciences Sviluppo and European Research Council.Research in contextEvidence before this studyWe searched PUBMED, MEDLINE and MedRxiv for clinical trials, meta-analyses and randomized controlled trials evaluating the antibody neutralization titres vs. different SARS-CoV-2 variants of concern obtained from subjects who received monoclonal antibodies for the treatment of COVID-19 using the following search terms: (“COVID-19” OR “SARS-CoV-2”) AND (“monoclonal antibody” OR “neutralising antibody”) AND (“variants” OR “variants of concern”). No relevant studies were identified.Added value of this studyThis is the first human study assessing safety, PK and neutralising potential of MAD0004J08, a monoclonal antibody against SARS-CoV-2 wild type Wuhan virus and variants of concern, administered intramuscularly at low dosages (48, 100 and 400 mg). MAD0004J08 showed to be safe and well tolerated in the tested dose range. Anti-spike antibodies were detected in the sera of tested SARS-CoV-2 negative healthy adults few hours post-injection. In addition, the sera obtained from MAD0004J08treated subjects, showed to have high neutralisation titres against the Wuhan virus, the B.1.1.7 (alpha), B.1.351 (beta) and B.1.1.248 (gamma) variants of concern.Implications of all the available evidenceA potent monoclonal antibody such as MAD0004J08, capable of neutralising multiple variants of concern of SARS-CoV-2 rapidly and long lastingly when given as a single intramuscular injection. The antibody, presently tested in a phase 2-3 efficacy trial, can be a major advancement in the prophylaxis and clinical management of COVID-19, because of its broad spectrum, ease of use in non-hospital settings and economic sustainability.

Coronavirus disease 2019 (COVID-19) outbreak is a public health emergency of international concern due to a highly pathogenic human coronavirus (HCoV), actually named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite much emerging data about the epidemiological association between cardiovascular diseases and COVID-19, little is still known atrial fibrillation (AF) and its optimal management in this clinical contest. The aim of our review is to describe the pharmacological interactions between cardiovascular drugs more commonly used in AF management and experimental COVID-19 therapies, based on European (EU) and Unites States (US) summaries of product characteristics.

Problems involving large-scale global optimization (LSGO) are becoming more and more frequent. For this reason, the last few years have seen an increasing number of researchers interested in improving optimization metaheuristics in such a way as to cope effectively with high-dimensional search domains. Among the techniques to enhance scalability, one of the most studied is Cooperative Coevolution (CC), an effective divide-and-conquer strategy for decomposing a large-scale problem into lower-dimensional subcomponents. However, despite the progress made in the LSGO field, one of such optimizations can still require a very high number of objective function evaluations. Therefore, when the evaluation of a candidate solution requires complex calculations, LSGO can become a challenging task. Nonetheless, to date few studies have investigated the application of optimization metaheuristics to objective functions that are simultaneously high-dimensional and computationally significant. To address such a research issue, this article investigates a surrogate-assisted CC (SACC) optimizer, in which fitness surrogates are exploited within the low-dimensional subcomponents resulting from the problem decomposition. The SACC algorithm is investigated on a rich test-bed composed of 1000-dimensional problems. According to the results, SACC is able to significantly boost the convergence of the CC optimizer, leading in many cases to a relevant computational gain.

This chapter describes the application of a recent compartment-based epidemiological model, namely the SEIAR (Susceptible-Exposed-Infectious-Asymptomatic-Recovered), to estimate the spreading of the coronavirus COVID-19 in Italy and in some of its regions. The model is here extended through the definition of a time-dependent dynamic social distancing (DSD) function, thus introducing the SEIAR–DSD model. To profitably use the SEIAR–DSD model, the most suitable values of its parameters must be found. This is performed through differential evolution, a heuristic optimization technique. This allows approximately evaluating, for each of the above-mentioned scenarios, the daily number of infectious individuals, the day(s) in which this number will reach its maximum, the corresponding value, and the future evolution of the spread.

Kvalitu života diabetických pacientů lze zlepšit vytvořením personalizovaného kontrolního algoritmu integrovaného do umělé slinivky břišní, schopného dávkovat inzulín. Klíčovou akcí při stavbě tohoto umělého zařízení je vymyslet účinný algoritmus pro předpovídání budoucích hladin glukózy. V tomto článku je navržena evoluční strategie, tj. Algoritmus gramatické evoluce, k odvození personalizovaného předpovědního modelu pro hodnocení hodnot glykémie v budoucnosti na základ minulých měření glukózy a znalost bazálních a infuzních hladin inzulínu a spotřeby jídla. Cílem je objevit modely, které jsou nejen interpretovatelné ale také s nízkou složitostí pro použití v řídicím algoritmu, který je hlavním prvkem umělé slinivky břišní. K vyhodnocení navrhovaného evolučního automatického postupu byla použita databáze v reálném světě, kterou tvoří diabetičtí pacienti 1. typu. The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin. A key action in the building of this artificial device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized forecasting model to evaluate blood glucose values in the future on the basis of the past glucose measurements, and the knowledge of the basal and infused insulin levels and of the food consumption. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by Type 1 diabetic patients has been employed to evaluate the proposed evolutionary automatic procedure.

Funding Acknowledgements No Funding OnBehalf RHYTHM DETECT study group Background A two-incision technique, in association with inter-muscular positioning of the subcutaneous defibrillator (S-ICD), is now the most frequently adopted implantation approach in Europe. Ultrasound-guided serratus anterior plane block (SAPB) has been proposed in order to provide anesthesia/analgesia during S-ICD implantation. Objective We performed a case-control analysis in which a standardized SAPB approach was compared with the typical local anesthesia and sedation approach. Methods 91 consecutive patients underwent implantation of an S-ICD with the SAPB approach for anesthesia/analgesia at 10 centers. The control group consisted of 55 consecutive patients who underwent S-ICD implantation with standard local approach. Results The mean procedure duration was 59 ± 15min in the SAPB group and 76 ± 23min in the control group (p Conclusions SAPB is feasible and effective in providing anesthesia/analgesia during S-ICD implantation. The procedures were successfully accomplished and no complications occurred in either group. However, SAPB was associated with lower pain levels, enabling the need for sedation to be reduced and more patients to remain awake. Moreover, it resulted in shorter procedure durations.

Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020). A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33 In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network. authorsversion published

Knihovna PaGMO nabízí několik optimizačních algoritmů pro stanovení optimálních parametrů black-boxmodelu. Takovým modelem může být například model pro homeostázu glukózy. Protože nás zajímá výpočet a predikce hladin glukózy u diabetických pacientů, testujeme knihovnu PaGMOpro tento konkrétní úkol. S využitím tří scénářů testujeme jednotlivé algoritmy PaGMO a porovnáváme je s našimi dřívějšími výsledky, které jsme získali pomocí de-randomizované meta diferenciální evoluce. Všechny testovací scénáře se zabývají skutečným případem zpracování signálu systému kontinuálního monitorování glukózy. Konkrétně se zabýváme rekonstrukcí a predikcí signálu. The PaGMO framework offers several optimizationalgorithms to determine optimal parameters of a black-boxmodel. Such a model could be, for example, that for glucose homeostasis. As we are concerned about calculating and predicting glucose levels for diabetic patients, we evaluate the PaGMO framework for this particular task. Using three scenarios, we test PaGMO’s individual algorithms and compare them to ourprevious results, which we obtained with de-randomized Meta-Differential Evolutions. All testing scenarios address real aspectsof processing a signal of the continuous glucose monitoring system. Specifically, we address signal reconstruction and prediction.

Tento článek ilustruje vývoj a použitelnost Evolučního Počítání pro zlepšení léčby pacientů s diabetem typu 1, kteří léčbu inzulínem. Jelikož je takové onemocnění spojeno se špatnou funkcí slinivky břišní, která produkuje nedostatečné množství inzulínu, způsob, jak zvýšit kvalitu života těchto pacientů, je vytvořit umělou slinivku schopnou uměle regulovat dávkování inzulínu. Cílem této práce je extrapolace regresního modelu, schopného odhadnout krevní glukózu (BG) pomocí měření intersticiální glukózy (IG) a jejich prvních derivací. V této studii demonstruujeme použitelnost tohoto přístupu. Zejména s ohledem na vysokou složitost vzájemných interakcí je navržena evoluční strategie, která extrapoluje matematický vztah mezi BG a IG a jeho derivací. Vypočítané modely jsou porovnány s jinými modely během experimentů na personalizovaných a globálních datech. This paper illustrates the development and the applicability of an Evolutionary Computation approach to enhance the treatment of Type-1 diabetic patients that necessitate insulin injections. In fact, being such a disease associated to a malfunctioning pancreas that generates an insufficient amount of insulin, a way to enhance the quality of life of these patients is to implement an artificial pancreas able to artificially regulate the insulin dosage. This work aims at extrapolating a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements and their numerical first derivatives. Such an approach represents a viable preliminary stage in building the basic component of this artificial pancreas. In particular, considered the high complexity of the reciprocal interactions, an evolutionary-based strategy is outlined to extrapolate a mathematical relationship between BG and IG and its derivative. The investigation is carried out about the accuracy of personalized models and of a global relationship model for all of the subjects under examination. The discovered models are assessed through a comparison with other models during the experiments on personalized and global data.

A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%. Fyziologický model zlepšuje poskytovanou zdravotní péči při konstrukci zdravotnického zařízení. Takový model zahrnuje řadu parametrů. Zatímco analytická metoda určuje parametry modelu, evoluční algoritmus je může dále vylepšovat. Protože vývojové algoritmy byly navrženy na generátorech náhodných čísel, jejich výsledky nejsou deterministické. To vzbuzuje obavy ohledně jejich použitelnosti na zdravotnické prostředky. Algoritmus zdravotnického zařízení musí produkovat výstup s minimální zaručenou přesností. Proto jsme použili de-randomizované sekvence na meta-diferenciální evoluci namísto použití generátoru náhodných čísel. Nakonec jsme navrhli optimalizační metodu založenou na zvětšování pomocí náhodných sekvencí jako alternativu k meta-diferenciální evoluci. Jako experimentální nastavení jsme předpovídali signál hladiny glukózy tak, aby pokrýval slepé okno signálu monitorování glukózy, které je výsledkem fyziologického zpoždění v transportu glukózy. Zcela de-randomizovaný diferenciální vývoj vykazoval stejnou přesnost a přesnost jako zcela nedeterministický diferenciální vývoj. Oba spočítali 93% koncentrací glukózy s relativní chybou menší nebo rovnou 15%.

In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide more effective and prompt care strategies, that cloud successfully influence patients’ life expectancy. However, the most performing systems implement the so called black-box approach, which do not provide explicit rules to reach a decision. This lack of interpretability, has hampered the acceptance of those systems by clinicians and their deployment on the field. In this context, we perform a thorough comparison of different machine learning (ML) techniques, whose classification results are characterized by different levels of interpretability. Such techniques were applied for automatically identify PD patients through the analysis of handwriting and drawing samples. Results analysis shows that white-box approaches, such as Cartesian Genetic Programming and Decision Tree, allow to reach a twofold goal: support the diagnosis of PD and obtain explicit classification models, on which only a subset of features (related to specific tasks) were identified and exploited for classification. Obtained classification models provide important insights for the design of non-invasive, inexpensive and easy to administer diagnostic protocols. Comparison of different ML approaches (in terms of both accuracy and interpretability) has been performed on the features extracted from the handwriting and drawing samples included in the publicly available PaHaW and NewHandPD datasets. The experimental findings show that the Cartesian Genetic Programming outperforms the white-box methods in accuracy and the black-box ones in interpretability.

Deep Neural Networks are being more and more widely used to perform several tasks over highly-sized datasets, one of them being classification. Finding good configurations for Deep Neural Network structures is a very important problem in general, and particularly in the medical domain. Currently, either trial-and-error methodologies or sampling-based ones are considered. This paper describes some preliminary steps towards effectively facing this task. The first step consists in the use of Differential Evolution, a kind of an Evolutionary Algorithm. The second lies in using a parallelized version in order to reduce the turnaround time. The preliminary results obtained here show that this approach can be useful in easily obtaining structures that allow increases in the network accuracy with respect to those provided by humans.

Pacienti s diabetem jsou vystaveni několika vážným zdravotním rizikům, které mohou vést k smrtelným následkům. Pro zvýšení kvality života těchto pacientů je nutné navrhnout umělou slinivku, která bude podávat inzulín v případě potřeby. Tento článek představuje algoritmus založený na genetickém programování pro extrapolaci regresního modelu schopného odhadnout hladinu glukózy v krvi (BG) pomocí měření intersticiální glukózy (IG) a jejich derivácí. Tento algoritmus představuje možný krok při budování základního prvku umělé slinivky, jmenovitě nové evoluční výpočetní metodologie pro odvození matematického vztahu mezi BG a IG. Navržený evoluční postup je vyhodnocen na základě reálné databáze, která se skládá z měření BG i IG u osob trpících diabetem 1. typu. Zjištěný model je validován porovnáním s dalšími technikami během experimentální fáze. The patients suffering from diabetes are subjected to several serious medical risks that can lead also to fatal consequences. To enhance the quality of life of these patients there is the necessity to devise an artificial pancreas able to inject an insulin bolus when needed. This paper presents a genetic-programming based algorithm to extrapolate a regression model able to estimate the blood glucose (BG) level through interstitial glucose (IG) measurements and their derivatives. This algorithm represents a possible step in building the fundamental element of such an artificial pancreas, namely a new evolutionary computation-based metodology to derive a mathematical relationship between BG and IG. The proposed evolutionary automatic procedure is evaluated on a real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other techniques during the experimental phase

The diabetes is correlated to a malfunction of the pancreas that produces very little or no insulin. A way to improve the quality of life of people with diabetes is to implement an artificial pancreas able to inject an insulin bolus when necessary. The aim of this paper is to devise a possibly step in constructing the fundamental element of such an artificial pancreas - estimation of the blood glucose (BG) through interstitial glucose (IG) measurements. In particular, a new methodology is presented to derive a mathematical relationship between BG and IG by exploiting the ability of the evolutionary techniques in solving this regression task. An automatic procedure is used to estimate the missing BG values within this database. To validate the discovered model a comparison with other models is carried out during the experimental phase.