Recent developments of the heritage and archaeology liaison office at the SOLEIL synchrotron

International audienc

Recent developments of the heritage and archaeology liaison office at the SOLEIL synchrotron

International audienc

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept

Objective: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. Methods: Two islet algorithm modelswere identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. Results: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, andoutperformed them under challenging conditions. Conclusion:This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. Significance:Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.Capteurs bio-électroniques intégrant l'algorithme des îlots pour le contrôle de la glycémie en boucle ouverte et fermé

Genome sequences of equine influenza A subtype H3N8 viruses by long read sequencing and functional characterization of the PB1-F2 virulence factor of A/equine/Paris/1/2018

International audienceAbstract Equine influenza virus (EIV) remains a persistent threat to equines, despite the availability of vaccines. Currently, strategies to monitor the virus and prevent any potential vaccine failure revolve around serological assays, RT‒qPCR amplification, and sequencing the viral hemagglutinin (HA) and neuraminidase (NA) genes. These approaches overlook the contribution of other viral proteins in driving virulence. This study assesses the potential of long-read nanopore sequencing for swift and precise sequencing of circulating equine influenza viruses. To this end, two French Florida Clade 1 strains, including the one circulating in winter 2018-2019 exhibiting more pronounced pathogenicity than usual, as well as the two currently used OIE-recommended vaccine strains, were sequenced. Our results demonstrated the reliability of this sequencing method in generating accurate sequences. Sequence analysis of HA revealed a subtle antigenic drift in the French EIV strains, with specific substitutions, such as T163I in A/equine/Paris/1/2018 and the N188T mutation in post-2015 strains; both substitutions were located in antigenic site B. Antigenic site E exhibited modifications in post-2018 strains, with the N63D substitution. Segment 2 sequencing also revealed that the A/equine/Paris/1/2018 strain encodes a longer variant of the PB1-F2 protein when compared to other Florida clade 1 strains (90 amino acids long versus 81 amino acids long). Further biological and biochemistry assays demonstrated that this PB1-F2 variant has enhanced abilities to abolish the mitochondrial membrane potential ΔΨm and permeabilize synthetic membranes. Altogether, our results highlight the interest in rapidly characterizing the complete genome of circulating strains with next-generation sequencing technologies to adapt vaccines and identify specific virulence markers of EIV

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients’ lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e ., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.Capteurs bio-électroniques intégrant l'algorithme des îlots pour le contrôle de la glycémie en boucle ouverte et fermé