Glucose variability assessment in diabetes mellitus monitoring and control

This dissertation is focused on the assessment of glucose variability (GV) in the treatment of the pathology of diabetes mellitus. GV is a risk factor for the development of diabetes complications, and its assessment combined with the evaluation of glycated hemoglobin levels is believed to be useful to characterize the functioning of glucose metabolism. Given the importance of GV in diabetes, a number of indicators to measure it from the retrospective analysis of sparse self-monitoring of blood glucose (SMBG) or continuous glucose monitoring (CGM) recordings have been proposed in the literature, but several issues are still open. For instance, some GV indicators have been developed specifically from SMBG data, and their use on CGM time-series has not been validated yet. Moreover, the availability of a large number of metrics to quantify GV gives rise to problems in terms of redundant conveyed information, and a compact way to extensively characterize GV would be desirable. Finally, the exploitation of CGM signals and GV to classify the metabolic condition of normal and diabetic subjects is a relatively unexplored problem that could deserve an investigation. These three topics are the object of this dissertation, which is specifically made up of six chapters whose content is briefly outlined below. Chapter 1 will describe the etiology of the different types of diabetes, discuss the development of diabetes complications, and introduce the technologies used to monitor blood glucose levels and the strategies exploited to manage the treatment of type 1 (T1DM) and type 2 (T2DM) diabetes mellitus. Chapter 2 will focus specifically on GV and its quantification, and, after highlighting the existing open issues, will precisely state the aims of the thesis. Chapter 3 will consider the problem of adapting some GV indicators originally developed and validated from SMBG, to the use with CGM signals. In particular, we will specifically look at low blood glucose index (LBGI) and high blood glucose index (HBGI), popular metrics that allow to provide a rapid classification of the quality of glucose control in diabetic subjects, and will provide alternate versions of these indicators adapted to the characteristics of CGMs by modeling the relationship between LBGI/HBGI values obtained from SMBG and CGM recordings. A dataset of 28 T1DM subjects monitored with both SMBG and CGM devices will be used to tune and assess the proposed methodology. Chapter 4 will address the issue of redundant information conveyed by the available GV indices by using the sparse principal component analysis (SPCA) technique as a tool to provide a parsimonious but still comprehensive characterization of GV in both T1DM and T2DM. Specifically, we will consider 25 GV indicators evaluated on CGM profiles acquired from 33 T1DM and 13 T2DM subjects as initial pool of variables. SPCA will be applied to this set of metrics and will be shown to be able to select a small subset of up to 10 indices that can save more than 60% of the original variance in both applications. The subset of metrics provided by SPCA can be used to parsimoniously describe GV in diabetes. Chapter 5 will be devoted to the assessment of the possibility of using the outputs from SPCA to build GV-based classifiers of the metabolic condition of normal and diabetic subjects. In particular, by resorting to a dataset of 55 T1DM subjects, 34 normal subjects at high risk of developing T2DM, 39 impaired glucose tolerance subjects, and 29 subjects with T2DM diagnosed, we will show that support vector machines are able to successfully classify the quality of glycemic control and the metabolic condition of disordered subjects, allowing to achieve an accuracy of classification always greater than 70%. The investigation will be performed using both the whole initial pool of 25 indicators and the parsimonious set selected by SPCA as features to design the classifiers; the fact that similar results were obtained in the two scenarios strengthens the speculation that the compact description of GV provided by SPCA is effectively comprehensive for characterizing the subjects' metabolic condition. Chapter 6 will close this dissertation, with a discussion on possible future developments of the presented investigations

Sviluppo di un nuovo algoritmo basato sul General Linear Model per la stima della risposta emodinamica da segnali di spettroscopia funzionale nel vicino infrarosso (fNIRS)

Il lavoro di tesi si propone di sviluppare un nuove algoritmo basato sul General Linear Model (GLM) per la stima della risposta emodinamica (HRF) da segnali di spettroscopia funzionale nel vicino infrarosso (fNIRS). La fNIRS e' una recente tecnica di neuroimaging che permette di monitorare l'ossigenazione cerebrale in modo completamente non invasivo. Tuttavia, il segnale fNIRS risulta etremamente rumoroso e l'HRF non e' visibile ad occhio nudo per la presenza del rumore sovrapposto; da qui la necessita' di sviluppare metodi di analisi ed elaborazione del segnale che consentano la stima dell'HRF da segnali fNIRS. In questa tesi si vuole in particolare validare un approccio basato sul GLM. Si noti come la possibilita' di ottenere una buona stima dell'HRF da dati fNIRS rappresenta un potente approccio di analisi funzionale del cervello totalmente non invasivaope

Challenges and opportunities for SERS in the infrared: materials and methods

In the wake of a global, heightened interest towards biomarker and disease detection prompted by the SARS-CoV-2 pandemic, surface enhanced Raman spectroscopy (SERS) positions itself again at the forefront of biosensing innovation. But is it ready to move from the laboratory to the clinic? This review presents the challenges associated with the application of SERS to the biomedical field, and thus, to the use of excitation sources in the near infrared, where biological windows allow for cell and through-tissue measurements. Two main tackling strategies will be discussed: (1) acting on the design of the enhancing substrate, which includes manipulation of nanoparticle shape, material, and supramolecular architecture, and (2) acting on the spectral collection set-up. A final perspective highlights the upcoming scientific and technological bets that need to be won in order for SERS to stably transition from benchtop to bedside

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed

Radiofrequency ablation for benign thyroid nodules

Benign thyroid nodules are an extremely common occurrence. Radiofrequency ablation (RFA) is gaining ground as an effective technique for their treatment, in case they become symptomatic. Here we review what are the current indications to RFA, its outcomes in terms of efficacy, tolerability, and cost, and also how it compares to the other conventional and experimental treatment modalities for benign thyroid nodules. Moreover, we will also address the issue of treating with this technique patients with cardiac pacemakers (PM) or implantable cardioverter-defibrillators (ICD), as it is a rather frequent occurrence that has never been addressed in detail in the literature

Endomyocardial biopsy in the clinical context: current indications and challenging scenarios

Endomyocardial biopsy (EMB) is an invasive procedure originally developed for the monitoring of heart transplant rejection. Over the year, this procedure has gained a fundamental complementary role in the diagnostic work-up of several cardiac disorders, including cardiomyopathies, myocarditis, drug-related cardiotoxicity, amyloidosis, other infiltrative and storage disorders, and cardiac tumours. Major advances in EMB equipment and techniques for histological analysis have significantly improved diagnostic accuracy of EMB. In recent years, advanced imaging modalities such as echocardiography with three-dimensional and myocardial strain analysis, cardiac magnetic resonance and bone scintigraphy have transformed the non-invasive approach to diagnosis and prognostic stratification of several cardiac diseases. Therefore, it emerges the need to re-define the current role of EMB for diagnostic work-up and management of cardiovascular diseases. The aim of this review is to summarize current knowledge on EMB in light of the most recent evidences and to discuss current indications, including challenging scenarios encountered in clinical practice

Full-Thickness Skin Burn Caused by Radiofrequency Ablation of a Benign Thyroid Nodule

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Identification of an iron–hepcidin complex

Following its identification as a liver-expressed antimicrobial peptide, the hepcidin peptide was later shown to be a key player in iron homoeostasis. It is now proposed to be the 'iron hormone' which, by interacting with the iron transporter ferroportin, prevents further iron import into the circulatory system. This conclusion was reached using the corresponding synthetic peptide, emphasizing the functional importance of the mature 25-mer peptide, but omitting the possible functionality of its maturation. From urine-purified native hepcidin, we recently demonstrated that a proportion of the purified hepcidin had formed iron-hepcidin complexes. This interaction was investigated further by computer modelling and, based on the sequence similarity of hepcidin with metallothionein, a three-dimensional model of hepcidin, containing one atom of iron, was constructed. To characterize these complexes further, the interaction with iron was analysed using different spectroscopic methods. Monoferric hepcidin was identified by MS, as were possibly other complexes containing two and three atoms of iron respectively, although these were present only in minor amounts. UV/visible absorbance and CD studies identified the iron-binding events which were facilitated at a physiological pH. EPR spectroscopy identified the ferric state of the bound metal, and indicated that the iron-hepcidin complex shares some similarities with the rubredoxin iron-sulfur complex, suggesting the presence of Fe(3+) in a tetrahedral sulfur co-ordination. The potential roles of iron binding for hepcidin are discussed, and we propose either a regulatory function in the maturation of pro-hepcidin into active hepcidin or as the necessary link in the interaction between hepcidin and ferroportin

Coronary Angiography After Transcatheter Aortic Valve Replacement (TAVR) to Evaluate the Risk of Coronary Access Impairment After TAVR‐in‐TAVR

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