Publications
199 results found
Wu Y, Jewell S, Xing X, et al., 2024, Real-time non-invasive imaging and detection of spreading depolarizations through EEG: an ultra-light explainable deep learning approach, IEEE Journal of Biomedical and Health Informatics, Pages: 1-12, ISSN: 2168-2208
A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension – frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.
Hamaoui K, Gowers S, Boutelle M, et al., 2024, Microdialysis in abdominal organ transplantation and the potential for integration with dynamic preservation platforms and post transplant monitoring, European Surgical Research, Vol: 65, Pages: 9-21, ISSN: 1421-9921
The perpetual organ shortage crisis worldwide has meant a paradigm shift in global thinking with subsequent expansion of the accepted criteria for an organ donor to meet the demand. Robust pre-transplant organ viability assessment is the next great challenge in the field of transplantation today. Organ preservation in the nature of static cold storage has reached its limits, and machine perfusion both cold and warm offers theoretically superior preservation and the potential to assess organs. Microdialysis is a novel technique with proven ability to allow remote assessment of tissue biochemistry and metabolism. It has been used in various pre-clinical and clinical models of abdominal organ preservation and transplantation. This review focuses on the use of microdialysis in the assessment of the kidney, liver, and pancreas, and where this novel technology is heading in the context of the assessing organ viability prior to and after transplantation.
Kassanos P, Gowers S, Boutelle M, 2023, Glucose and lactate amperometric sensors on a flexible printed circuit for low-cost sensing applications, 2023 IEEE SENSORS, Publisher: IEEE, ISSN: 2168-9229
Printed circuit board (PCB) and flexible printed circuit (FPC) technologies are widely available, established and low-cost technologies. They can be exploited to realize various types of sensors that can easily be co-integrated with the required instrumentation, to realize cost-efficient, compact sensing devices. This paper presents the realization of flexible glucose and lactate enzymatic sensors on an FPC electrode array with pure soft gold electrode finish, designed and manufactured with commercial tools and vendors. The glucose sensor was characterized up to 30 mM and the lactate up to 1 mM and where capable of providing a measurable response as a function of analyte concentration with measured currents of up to 300 nA and 60 nA, respectively.
Murray D-S, Stickel L, Boutelle M, 2023, Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body, ACS CHEMICAL NEUROSCIENCE, Vol: 14, Pages: 3599-3608, ISSN: 1948-7193
Robbins EM, Jaquins-Gerstl AS, Okonkwo DO, et al., 2023, Dexamethasone-Enhanced Continuous Online Microdialysis for Neuromonitoring of O<sub>2</sub> after Brain Injury, ACS CHEMICAL NEUROSCIENCE, Vol: 14, Pages: 2476-2486, ISSN: 1948-7193
Wang S, Liu S, Boutelle MG, 2023, Combining Complementary Models: Fusing CNNs, RNNs, and XGBoost for Enhanced Outcome Prediction of Comatose Patients after Heart Attack, ISSN: 2325-8861
Prognostication in comatose patients after cardiac arrest (CA) remains one of the biggest challenges for neurologists in the intensive care unit, as it shapes decisions about continuing or withdrawing life support. Electroencephalogram (EEG) provides valuable and non-invasive insights into patients' neurological status and has been used in many prediction models. However, traditional models often view EEG as stationary data, neglecting the dynamic patterns of EEG signals in response to internal and external perturbations. In addition, the importance of clinical data was underestimated in previous studies. We, team Data Doctors, took part in Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023, and proposed a prediction model based on data fusion, which scored 0.322 and ranked 29th in the official phase. We introduced a specialist system to combine various machine-learning frameworks, including a recurrent neural network (RNNs) for capturing dynamic EEG features, a convolutional neural network (CNNs) for identifying inter-channel EEG interactions, and an eXtreme Gradient Boosting (XGBoost) algorithm to synthesize these features for outcome prediction. The proposed model outperforms each single model, demonstrating the potential to improve outcome prediction accuracy and reliability by fusing complimentary results from different models.
Crook-Rumsey M, Musa AM, Iniesta R, et al., 2022, A shortened surface electromyography recording is sufficient to facilitate home fasciculation assessment, Muscle and Nerve, Vol: 66, Pages: 625-630, ISSN: 0148-639X
Introduction/AimsFasciculations are an early clinical hallmark of amyotrophic lateral sclerosis (ALS), amenable to detection by high-density surface electromyography (HDSEMG). In conjunction with the Surface Potential Quantification Engine (SPiQE), HDSEMG offers improved spatial resolution for the analysis of fasciculations. This study aims to establish an optimal recording duration to enable longitudinal remote monitoring in the home.MethodsTwenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent serial 30 min HDSEMG recordings from biceps brachii and gastrocnemii. SPiQE was independently applied to abbreviated epochs within each 30-min recording (0–5, 0–10, 0–15, 0–20, and 0–25 min), outputting fasciculation frequency, amplitude median and amplitude interquartile range. Bland–Altman plots and intraclass correlation coefficients (ICC) were used to assess agreement with the validated 30-min recording.ResultsIn total, 506 full recordings were included. The 5 min recordings demonstrated diverse and relatively poor agreement with the 30 min baselines across all parameters, muscles and patient groups (ICC = 0.32–0.86). The 15-min recordings provided more acceptable and stable agreement (ICC = 0.78–0.98), which did not substantially improve in longer recordings.DiscussionFor the detection and quantification of fasciculations in patients with ALS and BFS, HDSEMG recordings can be halved from 30 to 15 min without significantly compromising the primary outputs. Reliance on a shorter recording duration should lead to improved tolerability and repeatability among patients, facilitating longitudinal remote monitoring in patients' homes.
Zeng J, Kuang L, Cicatiello C, et al., 2022, A LoC Ion Imaging Platform for Spatio-Temporal Characterisation of Ion-Selective Membranes, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 16, Pages: 545-556, ISSN: 1932-4545
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Ming DK, Jangam S, Gowers SAN, et al., 2022, Real-time continuous measurement of lactate through a minimally invasive microneedle patch: a phase I clinical study, BMJ Innovations, Vol: 8, Pages: 87-94, ISSN: 2055-8074
Introduction Determination of blood lactate levels supports decision-making in a range of medical conditions. Invasive blood-sampling and laboratory access are often required, and measurements provide a static profile at each instance. We conducted a phase I clinical study validating performance of a microneedle patch for minimally invasive, continuous lactate measurement in healthy volunteers.Methods Five healthy adult participants wore a solid microneedle biosensor patch on their forearms and undertook aerobic exercise for 30 min. The microneedle biosensor quantifies lactate concentrations in interstitial fluid within the dermis continuously and in real-time. Outputs were captured as sensor current and compared with lactate concentrations from venous blood and microdialysis.Results The biosensor was well-tolerated. Participants generated a median peak venous lactate of 9.25 mmol/L (IQR 6.73–10.71). Microdialysate concentrations of lactate closely correlated with blood. Microneedle biosensor current followed venous lactate concentrations and dynamics, with good agreement seen in all participants. There was an estimated lag-time of 5 min (IQR −4 to 11 min) between microneedle and blood lactate measurements.Conclusion This study provides first-in-human data on use of a minimally invasive microneedle patch for continuous lactate measurement, providing dynamic monitoring. This low-cost platform offers distinct advantages to frequent blood sampling in a wide range of clinical settings, especially where access to laboratory services is limited or blood sampling is infeasible. Implementation of this technology in healthcare settings could support personalised decision-making in a variety of hospital and community settings.
Glaros KN, Rogers ML, Boutelle MG, et al., 2022, Sensors for Vital Signs: Oxygen Sensors, Handbook of Biochips: Integrated Circuits and Systems for Biology and Medicine, Pages: 291-314, ISBN: 9781441993182
Oxygen measurement in human tissues is very important for informed clinical decisions. This chapter provides an overview of several sensing paradigms that are used for this purpose, such as the Clark electrode, fluorescence quenching, PEBBLEs, optodes, near-infrared spectroscopy, and pulse oximetry. The challenges of designing monolithic low-power pulse oximeter biochips are discussed in detail. Design techniques focused on lowering the oximeter’s power consumption are presented, along with a biochip implementing the first sub-mW fully integrated pulse oximeter front-end.
Gifford EK, Robbins EM, Jaquins-Gerstl A, et al., 2021, Validation of Dexamethasone-Enhanced Continuous-Online Microdialysis for Monitoring Glucose for 10 Days after Brain Injury, ACS CHEMICAL NEUROSCIENCE, Vol: 12, Pages: 3588-3597, ISSN: 1948-7193
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- Citations: 2
Weddell T, Bashford J, Wickham A, et al., 2021, First-recruited motor units adopt a faster phenotype in amyotrophic lateral sclerosis, JOURNAL OF PHYSIOLOGY-LONDON, Vol: 599, Pages: 4117-4130, ISSN: 0022-3751
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- Citations: 5
Tyrrell JE, Petkos K, Drakakis EM, et al., 2021, Organic electrochemical transistor common‐source amplifier for electrophysiological measurements, Advanced Functional Materials, Vol: 31, Pages: 1-13, ISSN: 1616-301X
The portability of physiological monitoring has necessitated the biocompatibility of components used in circuitry local to biological environments. A key component in processing circuitry is the linear amplifier. Amplifier circuit topologies utilize transistors, and recent advances in bioelectronics have focused on organic electrochemical transistors (OECTs). OECTs have shown the capability to transduce physiological signals at high signal-to-noise ratios. In this study high-performance interdigitated electrode OECTs are implemented in a common source linear amplifier topology. Under the constraints of OECT operation, stable circuit component parameters are found, and OECT geometries are varied to determine the best amplifier performance. An equation is formulated which approximates transistor behavior in the linear, nonlinear, and saturation regimes. This equation is used to simulate the amplifier response of the circuits with the best performing OECT geometries. The amplifier figures of merit, including distortion characterizations, are then calculated using physical and simulation measurements. Based on the figures of merit, prerecorded electrophysiological signals from spreading depolarizations, electrocorticography, and electromyography fasciculations are inputted into an OECT linear amplifier. Using frequency filtering, the primary features of events in the bioelectric signals are resolved and amplified, demonstrating the capability of OECT amplifiers in bioelectronics.
Jewell S, Hobson S, Brewer G, et al., 2021, Development and evaluation of a method for automated detection of spreading depolarizations in the injured human brain, Neurocritical Care, Vol: 35, Pages: 160-175, ISSN: 1541-6933
BACKGROUND: Spreading depolarizations (SDs) occur in some 60% of patients receiving intensive care following severe traumatic brain injury and often occur at a higher incidence following serious subarachnoid hemorrhage and malignant hemisphere stroke (MHS); they are independently associated with worse clinical outcome. Detection of SDs to guide clinical management, as is now being advocated, currently requires continuous and skilled monitoring of the electrocorticogram (ECoG), frequently extending over many days. METHODS: We developed and evaluated in two clinical intensive care units (ICU) a software routine capable of detecting SDs both in real time at the bedside and retrospectively and also capable of displaying patterns of their occurrence with time. We tested this prototype software in 91 data files, each of approximately 24 h, from 18 patients, and the results were compared with those of manual assessment ("ground truth") by an experienced assessor blind to the software outputs. RESULTS: The software successfully detected SDs in real time at the bedside, including in patients with clusters of SDs. Counts of SDs by software (dependent variable) were compared with ground truth by the investigator (independent) using linear regression. The slope of the regression was 0.7855 (95% confidence interval 0.7149-0.8561); a slope value of 1.0 lies outside the 95% confidence interval of the slope, representing significant undersensitivity of 79%. R2 was 0.8415. CONCLUSIONS: Despite significant undersensitivity, there was no additional loss of sensitivity at high SD counts, thus ensuring that dense clusters of depolarizations of particular pathogenic potential can be detected by software and depicted to clinicians in real time and also be archived.
Booth MA, Gowers SAN, Hersey M, et al., 2021, Fiber-based electrochemical biosensors for monitoring pH and transient neurometabolic lactate., Analytical Chemistry, Vol: 93, Pages: 6646-6655, ISSN: 0003-2700
Developing tools that are able to monitor transient neurochemical dynamics is important to decipher brain chemistry and function. Multifunctional polymer-based fibers have been recently applied to monitor and modulate neural activity. Here, we explore the potential of polymer fibers comprising six graphite-doped electrodes and two microfluidic channels within a flexible polycarbonate body as a platform for sensing pH and neurometabolic lactate. Electrodes were made into potentiometric sensors (responsive to pH) or amperometric sensors (lactate biosensors). The growth of an iridium oxide layer made the fiber electrodes responsive to pH in a physiologically relevant range. Lactate biosensors were fabricated via platinum black growth on the fiber electrode, followed by an enzyme layer, making them responsive to lactate concentration. Lactate fiber biosensors detected transient neurometabolic lactate changes in an in vivo mouse model. Lactate concentration changes were associated with spreading depolarizations, known to be detrimental to the injured brain. Induced waves were identified by a signature lactate concentration change profile and measured as having a speed of ∼2.7 mm/min (n = 4 waves). Our work highlights the potential applications of fiber-based biosensors for direct monitoring of brain metabolites in the context of injury.
Olsen MH, Olesen ND, Karlsson M, et al., 2021, Randomized blinded trial of automated REBOA during CPR in a porcine model of cardiac arrest, RESUSCITATION, Vol: 160, Pages: 39-48, ISSN: 0300-9572
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- Citations: 10
Tyrrell J, Boutelle M, Campbell A, 2021, Measurement of electrophysiological signals in vitro using high-performance organic electrochemical transistors, Advanced Functional Materials, Vol: 31, Pages: 1-12, ISSN: 1616-301X
Biological environments use ions in charge transport for information transmission. The properties of mixed electronic and ionic conductivity in organic materials make them ideal candidates to transduce physiological information into electronically processable signals. A device proven to be highly successful in measuring such information is the organic electrochemical transistor (OECT). Previous electrophysiological measurements performed using OECTs show superior signal‐to‐noise ratios than electrodes at low frequencies. Subsequent development has significantly improved critical performance parameters such as transconductance and response time. Here, interdigitated‐electrode OECTs are fabricated on flexible substrates, with one such state‐of‐the‐art device achieving a peak transconductance of 139 mS with a 138 µs response time. The devices are implemented into an array with interconnects suitable for micro‐electrocorticographic application and eight architecture variations are compared. The two best‐performing arrays are subject to the full electrophysiological spectrum using prerecorded signals. With frequency filtering, kHz‐scale frequencies with 10 µV‐scale voltages are resolved. This is supported by a novel quantification of the noise, which compares the gate voltage input and drain current output. These results demonstrate that high‐performance OECTs can resolve the full electrophysiological spectrum and suggest that superior signal‐to‐noise ratios could be achieved in high frequency measurements of multiunit activity.
Wannop K, Bashford J, Wickham A, et al., 2020, Fasciculation analysis reveals a novel parameter that correlates with predicted survival in amyotrophic lateral sclerosis, Muscle and Nerve, Vol: 63, Pages: 392-396, ISSN: 0148-639X
IntroductionPrognostic uncertainty in amyotrophic lateral sclerosis (ALS) confounds clinical management planning, patient counseling, and trial stratification. Fasciculations are an early clinical hallmark of disease and can be quantified noninvasively. Using an innovative analytical method, we correlated novel fasciculation parameters with a predictive survival model.MethodsUsing high-density surface electromyography, we collected biceps recordings from ALS patients on their first research visit. By accessing an online survival prediction tool, we provided eight clinical and genetic parameters to estimate individual patient survival. Fasciculation analysis was performed using an automated algorithm (Surface Potential Quantification Engine), with a Cox proportional hazards model to calculate hazard ratios.ResultsThe median predicted survival for 31 patients was 41 (interquartile range, 31.5-57) months. Univariate hazard ratios were 1.09 (95% confidence interval [CI], 1.03-1.16) for the rate of change of fasciculation frequency (RoCoFF) and 1.10 (95% CI, 1.01-1.19) for the amplitude dispersion rate. Only the RoCoFF remained significant (P = .04) in a multivariate model.DiscussionNoninvasive measurement of fasciculations at a single time-point could enhance prognostic models in ALS, where higher RoCoFF values indicate shorter survival.
Tageldeen MK, Gowers SAN, Leong CL, et al., 2020, Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and cloud application, Journal of NeuroEngineering and Rehabilitation, Vol: 17, ISSN: 1743-0003
BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a cloud-based application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a cloud application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the cl
Masson J-F, Hashemi P, Boutelle MG, 2020, Analytical science in neurochemistry, ANALYST, Vol: 145, Pages: 3774-3775, ISSN: 0003-2654
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Hurst T, Pahl C, Tolias C, et al., 2020, Response to Stevens et al. (DOI: 10.1089/neu.2018.6175) Glucose Dynamics of Cortical Spreading Depolarization in Acute Brain Injury: A Systematic Review, JOURNAL OF NEUROTRAUMA, Vol: 37, Pages: 1266-1267, ISSN: 0897-7151
Bashford J, Masood U, Wickham A, et al., 2020, Fasciculations demonstrate daytime consistency in amyotrophic lateral sclerosis, Muscle and Nerve, Vol: 61, Pages: 745-750, ISSN: 0148-639X
IntroductionFasciculations represent early neuronal hyperexcitability in amyotrophic lateral sclerosis (ALS). To aid calibration as a disease biomarker, we set out to characterize the daytime variability of fasciculation firing.MethodsFasciculation awareness scores were compiled from 19 ALS patients. In addition, 10 ALS patients prospectively underwent high‐density surface electromyographic (HDSEMG) recordings from biceps and gastrocnemius at three time‐points during a single day.ResultsDaytime fasciculation awareness scores were low (mean: 0.28 muscle groups), demonstrating significant variability (coefficient of variation: 303%). Biceps HDSEMG recordings were highly consistent for fasciculation potential frequency (intraclass correlation coefficient [ICC] = 95%, n = 19) and the interquartile range of fasciculation potential amplitude (ICC = 95%, n = 19). These parameters exhibited robustness to observed fluctuations in data quality parameters. Gastrocnemius demonstrated more modest levels of consistency overall (44% to 62%, n = 20).DiscussionThere was remarkable daytime consistency of fasciculation firing in the biceps of ALS patients, despite sparse and intermittent awareness among patients’ accounts.
Moser N, Leong CL, Hu Y, et al., 2020, CMOS potentiometric FET array platform using sensor learning for multi-ion imaging., Analytical Chemistry, Vol: 92, Pages: 5276-5285, ISSN: 0003-2700
This work describes an array of 1024 Ion-Sensitive Field-Effect Transistors (ISFETs) using sensor learning techniques to perform multi-ion imaging for concurrent detection of potassium, sodium, calcium and hydrogen. Analyte specific ionophore membranes are deposited on the surface of the ISFET array chip, yielding pixels with quasi-Nernstian sensitivity to K+, Na+ or Ca2+. Uncoated pixels display pH sensitivity from the standard Si3N4 passivation layer. The platform is then trained by inducing a change in single ion concentration and measuring the responses of all pixels. Sensor learning relies on k-means clustering and DBSCAN to yield membrane mapping and sensitivity of each pixel to target electrolytes. We demonstrate multi-ion imaging with an average error of 3.7 % (K+), 4.6 % (Na+), and 1.8 % (pH) for each ion respectively, while Ca2+ incurs a larger error 24.2 % and hence is included to demonstrate versatility. We validate the platform with a brain dialysate fluid sample and demonstrate reading by comparing with a gold-standard spectrometry technique.
Gowers S, Samper I, Murray D-S, et al., 2020, Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model, The Analyst, Vol: 145, Pages: 1894-1902, ISSN: 0003-2654
This work describes a fully-integrated portable microfluidic analysis system for real-time monitoring of dynamic changes in glucose and lactate occurring in the brain as a result of cardiac arrest and resuscitation. Brain metabolites are sampled using FDA-approved microdialysis probes and coupled to a high temporal resolution 3D printed microfluidic chip housing glucose and lactate biosensors. The microfluidic biosensors are integrated with a wireless 2 channel potentiostat forming a compact analysis system that is ideal for use in a crowded operating theatre. Data are transmitted to a custom-written app running on a tablet for real-time visualisation of metabolic trends. In a proof of-concept porcine model of cardiac arrest, the integrated analysis system proved reliable in a challenging environment resembling a clinical setting; noise levels were found to be comparable with those seen in the lab and were not affected by major clinical interventions such as defibrillation of the heart. Using this system, we were able, for the first time, to measure changes in brain glucose and lactate levels caused by cardiac arrest and resuscitation; the system was sensitive to clinical interventions such as infusion of adrenaline. Trends suggest that cardiopulmonary resuscitation alone does not meet the high energy demands of the brain as metabolite levels only return to their values preceding cardiac arrest upon return of spontaneous circulation.
Bashford JA, Wickham A, Iniesta R, et al., 2020, Accurate interpretation of fasciculation frequency in amyotrophic lateral sclerosis hinges on both muscle type and stage of disease, BRAIN COMMUNICATIONS, Vol: 2
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Bashford J, Wickham A, Iniesta R, et al., 2020, Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis, Clinical Neurophysiology, Vol: 131, Pages: 265-273, ISSN: 1388-2457
ObjectivesFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). The Surface Potential Quantification Engine (SPiQE) is a novel analytical tool to identify fasciculation potentials from high-density surface electromyography (HDSEMG). This method was accurate on relaxed recordings amidst fluctuating noise levels. To avoid time-consuming manual exclusion of voluntary muscle activity, we developed a method capable of rapidly excluding voluntary potentials and integrating with the established SPiQE pipeline.MethodsSix ALS patients, one patient with benign fasciculation syndrome and one patient with multifocal motor neuropathy underwent monthly thirty-minute HDSEMG from biceps and gastrocnemius. In MATLAB, we developed and compared the performance of four Active Voluntary IDentification (AVID) strategies, producing a decision aid for optimal selection.ResultsAssessment of 601 one-minute recordings permitted the development of sensitive, specific and screening strategies to exclude voluntary potentials. Exclusion times (0.2–13.1 minutes), processing times (10.7–49.5 seconds) and fasciculation frequencies (27.4–71.1 per minute) for 165 thirty-minute recordings were compared. The overall median fasciculation frequency was 40.5 per minute (10.6–79.4 IQR).ConclusionWe hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings.SignificanceLongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
Bashford J, Wickham A, Iniesta R, et al., 2020, SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis (vol 130, pg 1083, 2019), Clinical Neurophysiology, Vol: 131, Pages: 350-350, ISSN: 1388-2457
[Correction to https://doi.org/10.1016/j.clinph.2019.03.032]
Tamborska A, Bashford J, Wickham A, et al., 2020, Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis, BRAIN COMMUNICATIONS, Vol: 2
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Bashford JA, Wickham A, Iniesta R, et al., 2020, The rise and fall of fasciculations in amyotrophic lateral sclerosis, BRAIN COMMUNICATIONS, Vol: 2
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- Citations: 17
Hamaoui K, Gowers S, Sandhu B, et al., 2020, Cold ischaemia time: is too long really too bad? studies using a porcine kidney ex-vivo reperfusion model, International Journal of Surgery Open, Vol: 23, Pages: 39-47, ISSN: 2405-8572
IntroductionPost-ischaemic hypothermic machine perfusion (HMP) may be beneficial in recovery of marginal kidney grafts. The full capacity of conventional HMP (with passive oxygenation) to recondition an organ has not been realised. We investigated whether HMP can ameliorate ischemic damage caused by extremely prolonged static cold storage (SCS).MethodsPorcine kidneys underwent 4-h (SCS4,n = 4) or 52-h (SCS52,n = 4) SCS, followed by 10 h of HMP and were then subjected to 2 h of isolated normothermic reperfusion (NRP).ResultsThere was a post-SCS graft weight loss in SCS52 vs SCS4 kidneys. SCS52 kidneys showed viable perfusion dynamics during HMP, with significantly shorter times to reach viable parameters vs SCS4 kidneys (p < 0.027). During NRP SCS52 kidneys demonstrated similar trends in perfusion dynamics, renal function, oxygen consumptions, lactate production, and tubular injury to SCS4 kidneys.ConclusionGraft weight loss after SCS, reducing resistance to perfusion, may facilitate better HMP dynamics and graft reconditioning. Clinicians utilising HMP should be aware of this phenomenon when using HMP in kidneys exposed to extreme periods of SCS. HMP after an extended period of SCS can resuscitate kidneys to a level equitable of viability as those after a short period of SCS. Utilising passive oxygenation however may be limiting such recovery and interventions utilising active oxygenation may provide benefit in such organs.
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