Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation.

Islet transplantation improves metabolic control in patients with unstable type 1 diabetes. Clinical outcomes have been improving over the last decade, and the widely used beta-score allows the evaluation of transplantation results. However, predictive pre-transplantation criteria of islet quality for clinical outcomes are lacking. In this proof-of-concept study, we examined whether characterization of the electrical activity of donor islets could provide a criterion. Aliquots of 8 human donor islets from the STABILOT study, sampled from islet preparations before transplantation, were characterized for purity and split for glucose-induced insulin secretion and electrical activity using multi-electrode-arrays. The latter tests glucose concentration dependencies, biphasic activity, hormones, and drug effects (adrenalin, GLP-1, glibenclamide) and provides a ranking of CHIP-scores from 1 to 6 (best) based on electrical islet activity. The analysis was performed online in real time using a dedicated board or offline. Grouping of beta-scores and CHIP-scores with high, intermediate, and low values was observed. Further analysis indicated correlation between CHIP-score and beta-score, although significance was not attained (R = 0.51, p = 0.1). This novel approach is easily implantable in islet isolation units and might provide means for the prediction of clinical outcomes. We acknowledge the small cohort size as the limitation of this pilot study.

Regularization for Unsupervised Learning of Optical Flow.

Regularization is an important technique for training deep neural networks. In this paper, we propose a novel shared-weight teacher-student strategy and a content-aware regularization (CAR) module. Based on a tiny, learnable, content-aware mask, CAR is randomly applied to some channels in the convolutional layers during training to be able to guide predictions in a shared-weight teacher-student strategy. CAR prevents motion estimation methods in unsupervised learning from co-adaptation. Extensive experiments on optical flow and scene flow estimation show that our method significantly improves on the performance of the original networks and surpasses other popular regularization methods. The method also surpasses all variants with similar architectures and the supervised PWC-Net on MPI-Sintel and on KITTI. Our method shows strong cross-dataset generalization, i.e., our method solely trained on MPI-Sintel outperforms a similarly trained supervised PWC-Net by 27.9% and 32.9% on KITTI, respectively. Our method uses fewer parameters and less computation, and has faster inference times than the original PWC-Net.

Semi-Supervised Training for Positioning of Welding Seams.

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

DOE-SLAM: Dynamic Object Enhanced Visual SLAM.

In this paper, we formulate a novel strategy to adapt monocular-vision-based simultaneous localization and mapping (vSLAM) to dynamic environments. When enough background features can be captured, our system not only tracks the camera trajectory based on static background features but also estimates the foreground object motion from object features. In cases when a moving object obstructs too many background features for successful camera tracking from the background, our system can exploit the features from the object and the prediction of the object motion to estimate the camera pose. We use various synthetic and real-world test scenarios and the well-known TUM sequences to evaluate the capabilities of our system. The experiments show that we achieve higher pose estimation accuracy and robustness over state-of-the-art monocular vSLAM systems.

Design of Potassium-Selective Mixed Ion/Electron Conducting Polymers.

An approach providing cation-selective poly-(3,4-ethylenedioxythiophene)(PEDOT):polyelectrolyte-mixed conductors is presented in this communication based on the structural modification of this ambivalent (ionic and electronic conductive) polymer complex. First, an 18-crown-6 moiety is integrated into the styrene sulfonate monomer structure as a specific metal cation scavenger particularly targeting K+ versus Na+ detection. This newly functionalized monomer is characterized by 1 H NMR titration to evaluate the ion selectivity. Aqueous PEDOT dispersion inks containing the polymeric ion-selective moieties are designed and their electrical and electrochemical properties analyzed. These biocompatible inks are the first proof-of-concept step towards ion selectivity in view of their interfacing with biological cells and microorgans of interest in the field of biosensors and physiology.

Texture features in the Shearlet domain for histopathological image classification.

BACKGROUND: A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning. METHODS: In this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB). RESULTS: Our feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%). CONCLUSIONS: Our proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals.

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Cell type-specific deletion in mice reveals roles for PAS kinase in insulin and glucagon production.

AIMS/HYPOTHESIS: Per-Arnt-Sim kinase (PASK) is a nutrient-regulated domain-containing protein kinase previously implicated in the control of insulin gene expression and glucagon secretion. Here, we explore the roles of PASK in the control of islet hormone release, by generating mice with selective deletion of the Pask gene in pancreatic beta or alpha cells. METHODS: Floxed alleles of Pask were produced by homologous recombination and animals bred with mice bearing beta (Ins1 (Cre); PaskBKO) or alpha (Ppg (Cre) [also known as Gcg]; PaskAKO) cell-selective Cre recombinase alleles. Glucose homeostasis and hormone secretion in vivo and in vitro, gene expression and islet cell mass were measured using standard techniques. RESULTS: Ins1 (Cre)-based recombination led to efficient beta cell-targeted deletion of Pask. Beta cell mass was reduced by 36.5% (p < 0.05) compared with controls in PaskBKO mice, as well as in global Pask-null mice (38%, p < 0.05). PaskBKO mice displayed normal body weight and fasting glycaemia, but slightly impaired glucose tolerance, and beta cell proliferation, after maintenance on a high-fat diet. Whilst glucose tolerance was unaffected in PaskAKO mice, glucose infusion rates were increased, and glucagon secretion tended to be lower, during hypoglycaemic clamps. Although alpha cell mass was increased (21.9%, p < 0.05), glucagon release at low glucose was impaired (p < 0.05) in PaskAKO islets. CONCLUSIONS/INTERPRETATION: The findings demonstrate cell-autonomous roles for PASK in the control of pancreatic endocrine hormone secretion. Differences between the glycaemic phenotype of global vs cell type-specific null mice suggest important roles for tissue interactions in the control of glycaemia by PASK.

Multilevel control of glucose homeostasis by adenylyl cyclase 8.

AIMS/HYPOTHESIS: Nutrient homeostasis requires integration of signals generated by glucose metabolism and hormones. Expression of the calcium-stimulated adenylyl cyclase ADCY8 is regulated by glucose and the enzyme is capable of integrating signals from multiple pathways. It may thus have an important role in glucose-induced signalling and glucose homeostasis. METHODS: We used pharmacological and genetic approaches in beta cells to determine secretion and calcium metabolism. Furthermore, Adcy8 knockout mice were characterised. RESULTS: In clonal beta cells, inhibitors of adenylyl cyclases or their downstream targets reduced the glucose-induced increase in cytosolic calcium and insulin secretion. This was reproduced by knock-down of ADCY8, but not of ADCY1. These agents also inhibited glucose-induced increase in cytosolic calcium and electrical activity in primary beta cells and similar effects were observed after ADCY8 knock-down. Moreover, insulin secretion was diminished in islets from Adcy8 knockout mice. These mice were glucose intolerant after oral or intraperitoneal administration of glucose whereas their levels of glucagon-like peptide-1 remained unaltered. Finally, we knocked down ADCY8 in the ventromedial hypothalamus to evaluate the need for ADCY8 in the central regulation of glucose homeostasis. Whereas mice fed a standard diet had normal glucose levels, high-fat diet exacerbated glucose intolerance and knock-down mice were incapable of raising their plasma insulin levels. Finally we confirmed that ADCY8 is expressed in human islets. CONCLUSIONS/INTERPRETATIONS: Collectively, our findings demonstrate that ADCY8 is required for the physiological activation of glucose-induced signalling pathways in beta cells, for glucose tolerance and for hypothalamic adaptation to a high-fat diet via regulation of islet insulin secretion.

Slow potentials encode intercellular coupling and insulin demand in pancreatic beta cells.

AIMS/HYPOTHESIS: Ion fluxes constitute a major integrative signal in beta cells that leads to insulin secretion and regulation of gene expression. Understanding these electrical signals is important for deciphering the endogenous algorithms used by islets to attain homeostasis and for the design of new sensors for monitoring beta cell function. METHODS: Mouse and human islets were cultured on multielectrode arrays (MEAs) for 3-13 days. Extracellular electrical activities received on each electrode were continuously amplified and recorded for offline characterisation. RESULTS: Differential band-pass filtering of MEA recordings of mouse islets showed two extracellular voltage waveforms: action potentials (lasting 40-60 ms) and very robust slow potentials (SPs, lasting 800-1,500 ms), the latter of which have not been described previously. The frequency of SPs directly correlated with glucose concentration, peaked at 10 mmol/l glucose and was further augmented by picomolar concentrations of glucagon-like peptide-1. SPs required the closure of ATP-dependent potassium channels as they were induced by glucose or glibenclamide but were not elicited by KCl-induced depolarisation. Pharmacological tools and the use of beta cell specific knockout mice showed that SPs reflected cell coupling via connexin 36. Moreover, increasing and decreasing glucose ramps showed hysteresis with reduced glucose sensitivity during the decreasing phase. SPs were also observed in human islets and could be continuously recorded over 24 h. CONCLUSIONS/INTERPRETATION: This novel electrical signature reflects the syncytial function of the islets and is specific to beta cells. Moreover, the observed hysteresis provides evidence for an endogenous algorithm naturally present in islets to protect against hypoglycaemia.

Calcium influx activates adenylyl cyclase 8 for sustained insulin secretion in rat pancreatic beta cells.

AIMS/HYPOTHESIS: Insulin is a key metabolic regulator in health and diabetes. In pancreatic beta cells, insulin release is regulated by the major second messengers Ca(2+) and cAMP: exocytosis is triggered by Ca(2+) and mediated by the cAMP/protein kinase A (PKA) signalling pathway. However, the causal link between these two processes in primary beta cells remains undefined. METHODS: Time-resolved confocal imaging of fluorescence resonance energy transfer signals was performed to visualise PKA activity, and combined membrane capacitance recordings were used to monitor insulin secretion from patch-clamped rat beta cells. RESULTS: Membrane depolarisation-induced Ca(2+) influx caused an increase in cytosolic PKA activity via activating a Ca(2+)-sensitive adenylyl cyclase 8 (ADCY8) subpool. Glucose stimulation triggered coupled Ca(2+) oscillations and PKA activation. ADCY8 knockdown significantly reduced the level of depolarisation-evoked PKA activation and impaired replenishment of the readily releasable vesicle pool. Pharmacological inhibition of PKA by two inhibitors reduced depolarisation-induced PKA activation to a similar extent and reduced the capacity for sustained vesicle exocytosis and insulin release. CONCLUSIONS/INTERPRETATION: Our findings suggest that depolarisation-induced Ca(2+) influx plays dual roles in regulating exocytosis in rat pancreatic beta cells by triggering vesicle fusion and replenishing the vesicle pool to support sustained insulin release. Therefore, Ca(2+) influx may be important for glucose-stimulated insulin secretion.

A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain MRI.

Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of potential PMG cases in MRI difficult. To enable the automatic detection of potential PMG cases, we propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM). Despite working with a small and imbalanced dataset our method achieves 88.07% recall at 71.86% precision. This will facilitate a computer-aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, our research is the first to apply machine learning techniques to identify PMG solely from MRI. Our code is available at: https://github.com/RichardChangCA/Deep-Contrastive-Metric-Learning-Method-to-Detect-Polymicrogyria-in-Pediatric-Brain-MRI. Our pediatric MRI dataset is available at: https://www.kaggle.com/datasets/lingfengzhang/pediatric-polymicrogyria-mri-dataset.

Extracellular electrophysiology on clonal human ß-cell spheroids.

Background: Pancreatic islets are important in nutrient homeostasis and improved cellular models of clonal origin may very useful especially in view of relatively scarce primary material. Close 3D contact and coupling between ß-cells are a hallmark of physiological function improving signal/noise ratios. Extracellular electrophysiology using micro-electrode arrays (MEA) is technically far more accessible than single cell patch clamp, enables dynamic monitoring of electrical activity in 3D organoids and recorded multicellular slow potentials (SP) provide unbiased insight in cell-cell coupling. Objective: We have therefore asked whether 3D spheroids enhance clonal ß-cell function such as electrical activity and hormone secretion using human EndoC-ßH1, EndoC-ßH5 and rodent INS-1 832/13 cells. Methods: Spheroids were formed either by hanging drop or proprietary devices. Extracellular electrophysiology was conducted using multi-electrode arrays with appropriate signal extraction and hormone secretion measured by ELISA. Results: EndoC-ßH1 spheroids exhibited increased signals in terms of SP frequency and especially amplitude as compared to monolayers and even single cell action potentials (AP) were quantifiable. Enhanced electrical signature in spheroids was accompanied by an increase in the glucose stimulated insulin secretion index. EndoC-ßH5 monolayers and spheroids gave electrophysiological profiles similar to EndoC-ßH1, except for a higher electrical activity at 3 mM glucose, and exhibited moreover a biphasic profile. Again, physiological concentrations of GLP-1 increased AP frequency. Spheroids also exhibited a higher secretion index. INS-1 cells did not form stable spheroids, but overexpression of connexin 36, required for cell-cell coupling, increased glucose responsiveness, dampened basal activity and consequently augmented the stimulation index. Conclusion: In conclusion, spheroid formation enhances physiological function of the human clonal ß-cell lines and these models may provide surrogates for primary islets in extracellular electrophysiology.

Electrophysiological characterisation of iPSC-derived human ß-like cells and an SLC30A8 disease model.

iPSC-derived human ß-like cells (BLC) hold promise for both therapy and disease modelling, but their generation remains challenging and their functional analyses beyond transcriptomic and morphological assessments remain limited. Here, we validate an approach using multicellular and single cell electrophysiological tools to evaluate function of BLCs from pioneer protocols that can be easily adapted to more differentiated BLCs. The Multi-Electrode Arrays (MEAs) measuring the extracellular electrical activity revealed that BLCs are electrically coupled, produce slow potential (SP) signals like primary ß-cells that are closely linked to insulin secretion. We also used high-resolution single-cell patch-clamp measurements to capture the exocytotic properties, and characterise voltage-gated sodium and calcium currents and found that they were comparable to those in primary ß and EndoC-ßH1 cells. The KATP channel conductance is greater than in human primary ß-cells which may account for the limited glucose responsiveness observed with MEA. We used MEAs to study the impact of the type 2 diabetes protective SLC30A8 allele (p.Lys34Serfs*50) and found that BLCs with this allele have stronger electrical coupling activity. Our data suggest that BLCs can be used to evaluate the functional impact of genetic variants on ß-cell function and coupling.

The Past, Present, and Future Role of Artificial Intelligence in Ventilation/Perfusion Scintigraphy: A Systematic Review.

Ventilation-perfusion (V/Q) lung scans constitute one of the oldest nuclear medicine procedures, remain one of the few studies performed in the acute setting, and are amongst the few performed in the emergency setting. V/Q studies have witnessed a long fluctuation in adoption rates in parallel to continuous advances in image processing and computer vision techniques. This review provides an overview on the status of artificial intelligence (AI) in V/Q scintigraphy. To clearly assess the past, current, and future role of AI in V/Q scans, we conducted a systematic Ovid MEDLINE(R) literature search from 1946 to August 5, 2022 in addition to a manual search. The literature was reviewed and summarized in terms of methodologies and results for the various applications of AI to V/Q scans. The PRISMA guidelines were followed. Thirty-one publications fulfilled our search criteria and were grouped into two distinct categories: (1) disease diagnosis/detection (N = 22, 71.0%) and (2) cross-modality image translation into V/Q images (N = 9, 29.0%). Studies on disease diagnosis and detection relied heavily on shallow artificial neural networks for acute pulmonary embolism (PE) diagnosis and were primarily published between the mid-1990s and early 2000s. Recent applications almost exclusively regard image translation tasks from CT to ventilation or perfusion images with modern algorithms, such as convolutional neural networks, and were published between 2019 and 2022. AI research in V/Q scintigraphy for acute PE diagnosis in the mid-90s to early 2000s yielded promising results but has since been largely neglected and thus have yet to benefit from today's state-of-the art machine-learning techniques, such as deep neural networks. Recently, the main application of AI for V/Q has shifted towards generating synthetic ventilation and perfusion images from CT. There is therefore considerable potential to expand and modernize the use of real V/Q studies with state-of-the-art deep learning approaches, especially for workflow optimization and PE detection at both acute and chronic stages. We discuss future challenges and potential directions to compensate for the lag in this domain and enhance the value of this traditional nuclear medicine scan.

Electrophysiological characterisation of iPSC-derived human ß-like cells and an SLC30A8 disease model.

iPSC-derived human ß-like cells (BLC) hold promise for both therapy and disease modelling, but their generation remains challenging and their functional analyses beyond transcriptomic and morphological assessments remain limited. Here, we validate an approach using multicellular and single cell electrophysiological tools to evaluate BLCs functions. The Multi-Electrode Arrays (MEAs) measuring the extracellular electrical activity revealed that BLCs are electrically coupled, produce slow potential (SP) signals like primary ß-cells that are closely linked to insulin secretion. We also used high-resolution single-cell patch-clamp measurements to capture the exocytotic properties, and characterize voltage-gated sodium and calcium currents. These were comparable to those in primary ß and EndoC-ßH1 cells. The KATP channel conductance is greater than in human primary ß cells which may account for the limited glucose responsiveness observed with MEA. We used MEAs to study the impact of the type 2 diabetes protective SLC30A8 allele (p.Lys34Serfs*50) and found that BLCs with this allele have stronger electrical coupling. Our data suggest that with an adapted approach BLCs from pioneer protocol can be used to evaluate the functional impact of genetic variants on ß-cell function and coupling.

Non-invasive long-term and real-time analysis of endocrine cells on micro-electrode arrays.

Non-invasive high-throughput and long-term monitoring of endocrine cells is important for drug research, phenotyping, tissue engineering and pre-transplantation quality control. Here we report a novel approach to obtain simultaneous long-term electrical recordings of different islet cell types using multi-electrode arrays. We implemented wavelet transforms to resolve the low signal/noise ratio inherent to these measurements and extracted on-line a signature specific of cell activity. The architecture employed allows multiplexing a large number of electrodes for high-throughput screening. This method should be of considerable advantage in endocrine research and may be extended to other excitable cells previously not accessible to the technique.

A charged prominence in the linker domain of the cysteine-string protein Cspα mediates its regulated interaction with the calcium sensor synaptotagmin 9 during exocytosis.

The cochaperone cysteine-string protein (Csp) is located on vesicles and participates in the control of neurotransmission and hormone exocytosis. Csp contains several domains, and our previous work demonstrated the requirement of the Csp linker domain in regulated exocytosis of insulin in rodent pancreatic ß cells. We now address the molecular details to gain insight into the sequence of events during exocytosis. According to pulldown experiments and in vitro binding assays, Cspα interacts indirectly with SNAP-25 and directly with the calcium sensor synaptotagmin 9 (Syt9), which could be an intermediate between the chaperone and the t-SNARE. The C(2)A calcium binding domain of Syt9 and the linker domain of Cspα constituted the minimal interacting module. FRET-FLIM experiments confirmed the interaction between Syt9 and Cspα. Moreover, the point mutation E93V in the linker domain of Cspα significantly reduced the interaction between the two proteins. Molecular modeling revealed that this point mutation abolished a charged prominence on the surface of Cspα required for interaction. Strikingly, free calcium in the physiological low micromolar range enhanced the interaction between Syt9 and the linker domain of Cspα in vitro. These data indicate that Cspα interacts with Syt9, and such a complex may be relevant in the calcium-mediated control of a late stage of exocytosis by triggering the specific recruitment of a folding catalyst at the fusion point.

Vertical Organic Electrochemical Transistors and Electronics for Low Amplitude Micro-Organ Signals.

Electrical signals are fundamental to key biological events such as brain activity, heartbeat, or vital hormone secretion. Their capture and analysis provide insight into cell or organ physiology and a number of bioelectronic medical devices aim to improve signal acquisition. Organic electrochemical transistors (OECT) have proven their capacity to capture neuronal and cardiac signals with high fidelity and amplification. Vertical PEDOT:PSS-based OECTs (vOECTs) further enhance signal amplification and device density but have not been characterized in biological applications. An electronic board with individually tuneable transistor biases overcomes fabrication induced heterogeneity in device metrics and allows quantitative biological experiments. Careful exploration of vOECT electric parameters defines voltage biases compatible with reliable transistor function in biological experiments and provides useful maximal transconductance values without influencing cellular signal generation or propagation. This permits successful application in monitoring micro-organs of prime importance in diabetes, the endocrine pancreatic islets, which are known for their far smaller signal amplitudes as compared to neurons or heart cells. Moreover, vOECTs capture their single-cell action potentials and multicellular slow potentials reflecting micro-organ organizations as well as their modulation by the physiological stimulator glucose. This opens the possibility to use OECTs in new biomedical fields well beyond their classical applications.

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 models were 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, and outperformed 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.