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.

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.

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.

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.

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.

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.

Electrochemotherapy with cisplatin enhances local control after surgical ablation of fibrosarcoma in cats: an approach to improve the therapeutic index of highly toxic chemotherapy drugs.

BACKGROUND: Cancer is one of the most difficult current health challenges, being responsible for millions of deaths yearly. Systemic chemotherapy is the most common therapeutic approach, and the prevailing orientation calls for the administration of the maximum tolerated dose; however, considerable limitations exist including toxicities to healthy tissues and low achievable drug concentrations at tumor sites. Electrochemotherapy (ECT) is a tumor treatment that combines the systemic or local delivery of anticancer drugs with the application of permeabilizing electric pulses. In this article we evaluate the capability of ECT to allow the use of cisplatin despite its high toxicity in a spontaneous feline model of soft tissue sarcoma. METHODS: A cohort of sixty-four cats with incompletely excised sarcomas were treated with cisplatin-based adjuvant ECT and monitored for side effects. Their response was compared to that of fourteen cats treated with surgery alone. RESULTS: The toxicities were minimal and mostly treated symptomatically. ECT resulted in increased local control (median not reached at the time of writing) with a mean time to recurrence of 666 days versus 180 of controls. CONCLUSIONS: We conclude that ECT is a safe and efficacious therapy for solid tumors; its use may be considered as part of strategies for the reintroduction of drugs with a narrow therapeutic index in the clinical protocols.

Automated parameter estimation of the Hodgkin-Huxley model using the differential evolution algorithm: application to neuromimetic analog integrated circuits.

We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.

Dynamic Uni- and Multicellular Patterns Encode Biphasic Activity in Pancreatic Islets.

Biphasic secretion is an autonomous feature of many endocrine micro-organs to fulfill physiological demands. The biphasic activity of islet ß-cells maintains glucose homeostasis and is altered in type 2 diabetes. Nevertheless, underlying cellular or multicellular functional organizations are only partially understood. High-resolution noninvasive multielectrode array recordings permit simultaneous analysis of recruitment, of single-cell, and of coupling activity within entire islets in long-time experiments. Using this unbiased approach, we addressed the organizational modes of both first and second phase in mouse and human islets under physiological and pathophysiological conditions. Our data provide a new uni- and multicellular model of islet ß-cell activation: during the first phase, small but highly active ß-cell clusters are dominant, whereas during the second phase, electrical coupling generates large functional clusters via multicellular slow potentials to favor an economic sustained activity. Postprandial levels of glucagon-like peptide 1 favor coupling only in the second phase, whereas aging and glucotoxicity alter coupled activity in both phases. In summary, biphasic activity is encoded upstream of vesicle pools at the micro-organ level by multicellular electrical signals and their dynamic synchronization between ß-cells. The profound alteration of the electrical organization of islets in pathophysiological conditions may contribute to functional deficits in type 2 diabetes.

Autonomous control of ventilation through closed-loop adaptive respiratory pacing.

Mechanical ventilation is the standard treatment when volitional breathing is insufficient, but drawbacks include muscle atrophy, alveolar damage, and reduced mobility. Respiratory pacing is an alternative approach using electrical stimulation-induced diaphragm contraction to ventilate the lung. Oxygenation and acid-base homeostasis are maintained by matching ventilation to metabolic needs; however, current pacing technology requires manual tuning and does not respond to dynamic user-specific metabolic demand, thus requiring re-tuning of stimulation parameters as physiological changes occur. Here, we describe respiratory pacing using a closed-loop adaptive controller that can self-adjust in real-time to meet metabolic needs. The controller uses an adaptive Pattern Generator Pattern Shaper (PG/PS) architecture that autonomously generates a desired ventilatory pattern in response to dynamic changes in arterial CO2 levels and, based on a learning algorithm, modulates stimulation intensity and respiratory cycle duration to evoke this ventilatory pattern. In vivo experiments in rats with respiratory depression and in those with a paralyzed hemidiaphragm confirmed that the controller can adapt and control ventilation to ameliorate hypoventilation and restore normocapnia regardless of the cause of respiratory dysfunction. This novel closed-loop bioelectronic controller advances the state-of-art in respiratory pacing by demonstrating the ability to automatically personalize stimulation patterns and adapt to achieve adequate ventilation.

Tuning of an Artificial Pancreas Controller: an in silico methodology based on clinically-relevant criteria.

This paper presents a methodology to tune an artificial pancreas controller by minimizing the time spent in endangering glycaemic ranges (hypo- and hyperglycaemia). The risk associated to the patient's glycaemia is evaluated with an objective metric (the blood glucose risk index), which has an established clinical relevance. The tuned controller is validated in the UVA/Padova environment where the resulting artificial pancreas achieves minimal glucose risk index in realistic 24-hour long scenarios with unannounced glucose intake.

In vivo validation of a new portable stimulator for chronic deep brain stimulation in freely moving rats.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is considered as a gold standard therapy for the alleviation of motor symptoms in Parkinson's disease (PD). This success paved the way to its application for other neurological and psychiatric disorders. In this context, we aimed to develop a rodent-specific stimulator with characteristics similar to those used in patients. NEW METHOD: We designed a stimulator that can be connected to an electrode container with options for bilateral or unilateral stimulation selection and offers a wide range of frequencies, pulse widths and intensities, constant current, biphasic current-control and charge balancing. Dedicated software was developed to program these parameters and the device was tested on a bilateral 6-hydroxydopamine (6-OHDA) rat model of PD. RESULTS: The equipment was well tolerated by the animals with a good general welfare. STN stimulation (130 Hz frequency, 0.06 ms pulse width, 150 µA average intensity) improved the motor deficits induced by 6-OHDA as it significantly increased the number of movements compared to the values obtained in the same animals without STN stimulation. Furthermore, it restored motor coordination by significantly increasing the time spent on the rotarod bar. CONCLUSION: We successfully developed and validated a new portable and programmable stimulator for freely moving rats that delivers a large range of stimulation parameters using bilateral biphasic current-control and charge balancing to maximize tissue safety. This device can be used to test deep brain stimulation in different animal models of human brain diseases.

Restoring Ventilatory Control Using an Adaptive Bioelectronic System.

Ventilatory pacing by electrical stimulation of the phrenic nerve or of the diaphragm has been shown to enhance quality of life compared to mechanical ventilation. However, commercially available ventilatory pacing devices require initial manual specification of stimulation parameters and frequent adjustment to achieve and maintain suitable ventilation over long periods of time. Here, we have developed an adaptive, closed-loop, neuromorphic, pattern-shaping controller capable of automatically determining a suitable stimulation pattern and adapting it to maintain a desired breath-volume profile on a breath-by-breath basis. The system adapts the pattern of stimulation parameters based on the error between the measured volume sampled every 40 ms and a desired breath volume profile. In vivo studies in anesthetized male Sprague-Dawley rats without and with spinal cord injury by spinal hemisection at C2 indicated that the controller was capable of automatically adapting stimulation parameters to attain a desired volume profile. Despite diaphragm hemiparesis, the controller was able to achieve a desired volume in the injured animals that did not differ from the tidal volume observed before injury (p = 0.39). Closed-loop adaptive pacing partially mitigated hypoventilation as indicated by reduction of end-tidal CO2 values during pacing. The closed-loop controller was developed and parametrized in a computational testbed before in vivo assessment. This bioelectronic technology could serve as an individualized and autonomous respiratory pacing approach for support or recovery from ventilatory deficiency.

An IC-based controllable stimulator for respiratory muscle stimulation investigations.

Functional Electrical Stimulation can be used to restore motor functions loss consecutive to spinal cord injury, such as respiratory deficiency due to paralysis of ventilatory muscles. This paper presents a fully configurable IC-centered stimulator designed to investigate muscle stimulation paradigms. It provides 8 current stimulation channels with high-voltage compliance and real-time operation capabilities, to enable a wide range of FES applications. The stimulator can be used in a standalone mode, or within a closed-loop setup. Primary in vivo results show successful drive of respiratory muscles stimulation using a computer-based dedicated controller.

Simultaneous monitoring of single cell and of micro-organ activity by PEDOT:PSS covered multi-electrode arrays.

Continuous and long-term monitoring of cellular and micro-organ activity is required for new insights into physiology and novel technologies such as Organs-on-Chip. Moreover, recent advances in stem cell technology and especially in the field of diabetes call for non-invasive approaches in quality testing of the large quantities of surrogate pancreatic islets to be generated. Electrical activity of such a micro-organ results in single cell action potentials (APs) of high frequency and in low frequency changes in local field potentials (slow potentials or SPs), reflecting coupled cell activity and overall organ physiology. Each of them is indicative of different physiological stages in islet activation. Action potentials in islets are of small amplitude and very difficult to detect. The use of PEDOT:PSS to coat metal electrodes is expected to reduce noise and results in a frequency-dependent change in impedance, as shown here. Whereas detection of high-frequency APs improves, low frequency SPs are less well detected which is, however, an acceptable trade off in view of the strong amplitude of SPs. Using a dedicated software, recorded APs and SPs can be automatically diagnosed and analyzed. Concomitant capture of the two signals will considerably increase the diagnostic power of monitoring islets and islet surrogates in fundamental research as well as drug screening or the use of islets as biosensors.

Bio-Inspired Controller on an FPGA Applied to Closed-Loop Diaphragmatic Stimulation.

Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals. The open-loop version of the bio-inspired controller was implemented on the FPGA. Electrical test bench characterizations confirmed the system functionality. Open and closed-loop paradigm simulations were simulated to test the FPGA system real-time behavior using the rat computational model. The closed-loop system monitors breathing and changes in respiratory demands to drive diaphragmatic stimulation. The simulated results inform future acute animal experiments and constitute the first step toward the development of a neuromorphic, adaptive, compact, low-power, implantable device. The bio-inspired hardware design optimizes the FPGA resource and time costs while harnessing the computational power of spike-based neuromorphic hardware. Its real-time feature makes it suitable for in vivo applications.

An Embedded Deep Brain Stimulator for Biphasic Chronic Experiments in Freely Moving Rodents.

This paper describes a Deep Brain Stimulation device, portable, for chronic experiments on rodents in the context of Parkinson's disease. Our goal is to equip the animal with a device that mimics the human therapeutic conditions. It implies to respect a set of properties such as bilateral current-mode and charge-balanced stimulation, as well as programmability, low power consumption and re-usability to finally reach a suitable weight for long-term experiments. After the analysis of the solutions found in the literature, the full design of the device is explained. First, the stimulation front-end circuit driven by a processor unit, then the choice of supply sources which is a critical point for the weight and life-time of our system. Our low cost system has been realized using commercial discrete components and the overall power consumption was minimized. We achieved 6 days of maximal current stimulation with the chosen battery for a weight of 13.8 g . Finally, the device was carried out in vivo on rats during a 3 weeks experiment as the used implantation technique allows battery changing. This experiment also permits to emphasize the mechanical aspects including the packaging and electrodes holding.

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord Using Intraspinal Electrical Microstimulation Driven by a Digital Neuromorphic CPG.

Neural prostheses based on electrical microstimulation offer promising perspectives to restore functions following lesions of the central nervous system (CNS). They require the identification of appropriate stimulation sites and the coordination of their activation to achieve the restoration of functional activity. On the long term, a challenging perspective is to control microstimulation by artificial neural networks hybridized to the living tissue. Regarding the use of this strategy to restore locomotor activity in the spinal cord, to date, there has been no proof of principle of such hybrid approach driving intraspinal microstimulation (ISMS). Here, we address a first step toward this goal in the neonatal rat spinal cord isolated ex vivo, which can display locomotor-like activity while offering an easy access to intraspinal circuitry. Microelectrode arrays were inserted in the lumbar region to determine appropriate stimulation sites to elicit elementary bursting patterns on bilateral L2/L5 ventral roots. Two intraspinal sites were identified at L1 level, one on each side of the spinal cord laterally from the midline and approximately at a median position dorso-ventrally. An artificial CPG implemented on digital integrated circuit (FPGA) was built to generate alternating activity and was hybridized to the living spinal cord to drive electrical microstimulation on these two identified sites. Using this strategy, sustained left-right and flexor-extensor alternating activity on bilateral L2/L5 ventral roots could be generated in either whole or thoracically transected spinal cords. These results are a first step toward hybrid artificial/biological solutions based on electrical microstimulation for the restoration of lost function in the injured CNS.

Guiding pancreatic beta cells to target electrodes in a whole-cell biosensor for diabetes.

We are developing a cell-based bioelectronic glucose sensor that exploits the multi-parametric sensing ability of pancreatic islet cells for the treatment of diabetes. These cells sense changes in the concentration of glucose and physiological hormones and immediately react by generating electrical signals. In our sensor, signals from multiple cells are recorded as field potentials by a micro-electrode array (MEA). Thus, cell response to various factors can be assessed rapidly and with high throughput. However, signal quality and consequently overall sensor performance rely critically on close cell-electrode proximity. Therefore, we present here a non-invasive method of further exploiting the electrical properties of these cells to guide them towards multiple micro-electrodes via electrophoresis. Parameters were optimized by measuring the cell's zeta potential and modeling the electric field distribution. Clonal and primary mouse or human ß-cells migrated directly to target electrodes during the application of a 1 V potential between MEA electrodes for 3 minutes. The morphology, insulin secretion, and electrophysiological characteristics were not altered compared to controls. Thus, cell manipulation on standard MEAs was achieved without introducing any external components and while maintaining the performance of the biosensor. Since the analysis of the cells' electrical activity was performed in real time via on-chip recording and processing, this work demonstrates that our biosensor is operational from the first step of electrically guiding cells to the final step of automatic recognition. Our favorable results with pancreatic islets, which are highly sensitive and fragile cells, are encouraging for the extension of this technique to other cell types and microarray devices.