Case Report: Liver Cysts and SARS-CoV-2: No Evidence of Virus in Cystic Fluid

Background: In December 2019, an outbreak of pneumonia, caused by a new type of coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It quickly spread worldwide, resulting in a pandemic. The clinical manifestations of SARS-CoV-2 range from mild non-specific symptoms to severe pneumonia with organ function damage. In addition, up to 60% of patients have liver impairment or dysfunction, confirmed by several studies by the presence of SARS-CoV-2 in the liver tissue.Methods: We report two cases of symptomatic liver cyst requiring fenestration after recent SARS-CoV-2 infection. Both patients had hospital admission due to documented SARS-CoV-2 infection. Recently, after the infection, they developed symptoms caused by an enlarged hepatic cyst: one had abdominal pain, and the other had jaundice. They underwent surgery after two negative swab tests for SARS-CoV-2.Results: Cystic fluid was sent for microbiological test, and real-time fluorescence polymerase chain reaction COVID-19 nucleic-acid assay of the cyst fluid was found to be negative in both cases.Discussion: Although there are no current data that can document a viral contamination of cystic fluid, there are data that document a hepatotropism of COVID-19 virus. Herein we report that after viral clearance at pharyngeal and nasal swab, there is no evidence of viral load in such potential viral reservoir

Machine Learning Based Techniques for the Design of Personalized Insulin Bolus Calculators in Type 1 Diabetes Therapy

Negli ultimi anni, l'incidenza e la prevalenza del diabete di tipo 1 (T1D) sono in aumento in tutto il mondo. Oltre all'onere economico legato al T1D, la gestione e il trattamento di questa malattia richiedono un grande impegno da parte delle persone che ne sono affette, poiché il loro organismo non è più in grado di produrre insulina, uno degli ormoni chiave nella regolazione della glicemia (BG). La mancanza di produzione endogena di insulina provoca livelli elevati di glicemia e, in particolare, iperglicemia, una condizione che può portare a diverse complicazioni cardiovascolari a lungo termine, come la retinopatia e la nefropatia. Pertanto, le persone affette da T1D necessitano di una terapia a vita, che si basa sulla somministrazione di insulina esogena. Tuttavia, la gestione quotidiana della T1D ha un impatto significativo sulla qualità di vita del paziente, a causa del numero di compiti richiesti per ottenere una corretta regolazione del livello di glucosio. Infatti, uno dei maggiori ostacoli a un controllo glicemico ottimale è rappresentato dalla stima di una corretta dose di insulina prandiale, che viene iniettata per contrastare l'escursione della glicemia dopo un pasto. Un accurato dosaggio dell'insulina al momento del pasto nella terapia dei T1D è fondamentale per evitare eventi di ipo- o iperglicemia postprandiale, causati rispettivamente da un sovradosaggio o da un sottodosaggio. Secondo le linee guida raccomandate per la gestione del T1D, la quantità di insulina al pasto dovrebbe essere calcolata seguendo una formula standard empirica (SF), che potrebbe portare a un dosaggio subottimale a causa di diverse ragioni, tra cui, soprattutto, l'incapacità di tenere conto delle informazioni rilevanti relative alla dinamica del glucosio e la mancanza di individualizzazione. La stima dell'insulina prandiale è un compito altamente dipendente dal paziente, che deve essere specificamente adattato alle condizioni dell'individuo al momento del pasto, non solo integrando parametri personalizzati, ma anche regolando la dose in base all'andamento attuale della glicemia. Tali informazioni sulla dinamica della glicemia e, in particolare, sulla sua velocità di variazione sono fornite, in tempo reale, dai sensori per il monitoraggio continuo del glucosio (CGM), dispositivi minimamente invasivi che stanno diventando un elemento chiave nella terapia del T1D. La disponibilità in tempo reale di informazioni sulla dinamica del glucosio fornite dai sistemi CGM, insieme alla possibilità di sfruttare dispositivi intelligenti per la somministrazione di insulina, che potrebbero potenzialmente integrare una nuova tecnica di dosaggio, ha favorito lo sviluppo di nuovi approcci per regolare la quantità di SF in base alle informazioni sul glucosio fornite da questi sensori. Tuttavia, la derivazione degli approcci allo stato dell'arte proposti per correggere l'SF è stata principalmente empirica, suggerendo che ci sarebbe un margine di miglioramento se si adottasse una metodologia di modellazione sistematica. Pertanto, il lavoro presentato in questa tesi mira a proporre tecniche di dosaggio dell'insulina durante il pasto, che siano efficaci e personalizzate, e che tengano conto sia delle informazioni derivate dal CGM sia dello stato specifico del pasto dell'individuo, per ottimizzare tale dosaggio, sfruttando algoritmi di apprendimento automatico e di apprendimento per rinforzo.In recent years, the incidence and prevalence of type 1 diabetes (T1D) are increasing worldwide. In addition to the economic burden related to T1D, the management and treatment of such a disease require lots of effort from those people who are affected, as their body is no longer able to produce insulin, one of the key hormones in blood glucose (BG) regulation. The lack of endogenous insulin production results in elevated BG levels and, in particular, in hyperglycemia, a condition that can lead to several long-term cardiovascular complications, such as retinopathy and nephropathy. Therefore, people affected by T1D need lifelong therapy, which relies on exogenous insulin administrations. However, daily management of T1D significantly impacts on patient's quality of life, due to the number of tasks required to achieve proper glucose level regulation. As a matter of fact, one of the major obstacles to optimal glycemic control is represented by the estimation of a correct prandial insulin dose, which is injected to counteract the BG excursion following a meal. Indeed, an accurate mealtime insulin dosing in T1D therapy is crucial to avoid postprandial hypo- or hyperglycemic events, caused by an over- or under-dosage respectively. According to the recommended guidelines for T1D management, the mealtime insulin amount should be calculated following an empirical standard formula (SF), which could lead to a suboptimal dosage due to several reasons, including, above all, the inability of accounting for relevant information related to the glucose dynamics and a lack of individualization. Prandial insulin estimation is a highly patient-dependent task which should be specifically tailored to the individuals' mealtime condition, not only by integrating relevant personalized parameters but also by adjusting the dose based on the current BG trend. Such information on BG dynamics and, in particular, its rate of change is provided, in real-time, by continuous glucose monitoring (CGM) sensors, minimally invasive devices that are becoming a key element in T1D therapy. The real-time availability of information on glucose dynamics provided by CGM systems, along with the possibility of leveraging smart insulin delivery devices, which could potentially integrate a novel dosing technique, fostered the development of new approaches to adjust the SF amount according to the glucose information provided by these sensors. However, the derivation of the proposed state-of-art approaches aimed at correcting the SF has mainly been empirical, suggesting that there would be room for improvement should a systematic modelling methodology be adopted. Therefore, the work presented in this thesis aims at proposing effective and personalized mealtime insulin dosing techniques, which take into account both the CGM-derived information and the specific mealtime status of the individual, to optimize such a dosage, by leveraging machine learning and reinforcement learning algorithms

An Ensemble Learning Algorithm Based on Dynamic Voting for Targeting the Optimal Insulin Dosage in Type 1 Diabetes Management

: People with type 1 diabetes (T1D) need exogenous insulin administrations several times a day. The amount of injected insulin is key for maintaining the concentration of blood glucose (BG) within a physiological safe range. According to well-established clinical guidelines, insulin dosing at mealtime is calculated through an empirical formula which, however, does not take advantage of the knowledge of BG trend provided in real-time by continuous glucose monitoring (CGM) sensors. To overcome suboptimal insulin dosage, we recently used machine learning techniques to build two new models, one linear and one nonlinear, which incorporate BG trend information.In this work, we propose an ensemble learning method for mealtime insulin bolus estimation based on dynamic voting, which combines the two models by taking advantage of where each alternative performs better. Being the resulting model black-box, a tool that enables its interpretability was applied to evaluate the contribution of each feature. The proposed model was trained using a synthetic dataset having information on 100 virtual subjects with different mealtime conditions, and its performance was evaluated within a simulated environment.The benefit given by the ensemble method compared to the single models was confirmed by the high time within the target glycemic range, and the trade-off reached in terms of time spent below and above this range. Moreover, the model interpretation pointed out the key role played by the information on BG dynamics in the estimation of insulin dosage

A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q-Learning to Improve Type 1 Diabetes Management

Mealtime insulin dosing is a major challenge for people living with type 1 diabetes (T1D). This task is typically performed using a standard formula that, despite containing some patient-specific parameters, often leads to sub-optimal glucose control due to lack of personalization and adaptation. To overcome the previous limitations here we propose an individualized and adaptive mealtime insulin bolus calculator based on double deep Q-learning (DDQ), which is tailored to the patient thanks to a personalization procedure relying on a two-step learning framework. The DDQ-learning bolus calculator was developed and tested using the UVA/Padova T1D simulator modified to reliably mimic real-world scenarios by introducing multiple variability sources impacting glucose metabolism and technology. The learning phase included a long-term training of eight sub-population models, one for each representative subject, selected thanks to a clustering procedure applied to the training set. Then, for each subject of the testing set, a personalization procedure was performed, by initializing the models based on the cluster to which the patient belongs. We evaluated the effectiveness of the proposed bolus calculator on a 60-day simulation, using several metrics representing the goodness of glycemic control, and comparing the results with the standard guidelines for mealtime insulin dosing. The proposed method improved the time in target range from 68.35% to 70.08% and significantly reduced the time in hypoglycemia (from 8.78% to 4.17%). The overall glycemic risk index decreased from 8.2 to 7.3, indicating the benefit of our method when applied for insulin dosing compared to standard guidelines

A New Decision Support System for Type 1 Diabetes Management

: Type 1 diabetes (T1D) is a chronic life-threatening metabolic condition which needs to be accurately and continuously managed with care by multiple daily exogenous insulin injections, frequent blood glucose concentration monitoring, ad-hoc diet, and physical activity. In the last decades, new technologies, such as continuous glucose monitoring sensors, eased the burden for T1D patients and opened new therapy perspectives by fostering the development of decision support systems (DSS). A DSS for T1D should be able to provide patients with advice aimed at improving metabolic control and reducing the number of actions related to therapy handling. Major challenges are the vast intra-/inter-subject physiological variability and the many factors that impact glucose metabolism. The present work illustrates a new DSS for T1D management. The algorithmic core includes a module for optimal, personalized, insulin dose calculation and a module that triggers the assumption of rescue carbohydrates to avoid/mitigate impending hypoglycemic events. The algorithms are integrated within a prototype communication platform that comprises a mobile app, a real-time telemonitoring interface, and a cloud server to safely store patients' data. Tests made in silico show that the use of the new algorithms lead to metabolic control indices significantly better than those obtained by the standard care for T1D. The preliminary test of the prototype platform suggests that it is robust, performant, and well-accepted by both patients and clinicians. Future work will focus on the refinement of the communication platform and the design of a clinical trial to assess the system effectiveness in real-life conditions.Clinical Relevance- The presented DSS is a promising tool to facilitate T1D daily management and improve therapy efficacy

Laparoscopic microwave ablation in patients with hepatocellular carcinoma: A prospective cohort study

none11noOBJECTIVES: There are no prospective studies of laparoscopic microwave (MW) ablation in patients with hepatocellular carcinoma (HCC). The aim of this study was to demonstrate the safety and efficacy of laparoscopic MW ablation. METHODS: A prospective study group of consecutive HCC patients considered ineligible for liver resection and/or percutaneous ablation was conducted from December 2009 to December 2010. Short-term (3-month) outcomes included a centralized revision of radiological response, mortality and morbidity. Mid-term (24-month) outcomes included time to recurrence in the study group compared with that in a cohort of consecutive patients treated with laparoscopic radiofrequency (RF) ablation using propensity score analysis. RESULTS: A total of 42 patients were enrolled. Their median age was 64 years; 67% were positive for hepatitis C virus; 33% were of Child-Pugh class B status; the median tumour diameter was 2.5 cm, and 48% of patients had multinodular HCC. In 47 of 50 (94%) nodules treated with MW ablation, a complete radiological response was observed at 3 months. There was no perioperative mortality. The overall morbidity rate was 24%. The 2-year survival rate was 79% and the 2-year recurrence rate was 55%. Using propensity score analysis (in 28 MW ablation patients and 28 RF ablation controls), 2-year recurrence rates were 55% in the MW ablation group and 77% in the control group (P = 0.03). CONCLUSIONS: Laparoscopic MW ablation is a safe and effective therapeutic option for selected HCC patients who are ineligible for liver resection and/or percutaneous ablation.restrictedCillo, Umberto; Noaro, Giulia; Vitale, Alessandro; Neri, Daniele; D'Amico, Francesco; Gringeri, Enrico; Farinati, Fabio; Vincenzi, Valter; Vigo, Mario; Zanus, Giacomo; Benvegnu' LuisaCillo, Umberto; Noaro, Giulia; Vitale, Alessandro; Neri, Daniele; D'Amico, FRANCESCO ENRICO; Gringeri, Enrico; Farinati, Fabio; Vincenzi, Valter; Vigo, Mario; Zanus, Giacomo; Benvegnu', Luis