Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia-ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80-120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Time and sex dependent effects of magnesium sulphate on post-asphyxial seizures in preterm fetal sheep.

KEY POINTS: We evaluated the effect of magnesium sulphate (MgSO4 ) on seizures induced by asphyxia in preterm fetal sheep. MgSO4 did not prevent seizures, but significantly reduced the total duration, number of seizures, seizure amplitude and average seizure burden. Saline-asphyxia male fetuses had significantly more seizures than female fetuses, but male fetuses showed significantly greater reduction in seizures during MgSO4 infusion than female fetuses. A circadian profile of seizure activity was observed in all fetuses, with peak seizures seen around 04.00-06.00 h on the first and second days after the end of asphyxia. This study is the first to demonstrate that MgSO4 has utility as an anti-seizure agent after hypoxia-ischaemia. More information is needed about the mechanisms mediating the effect of MgSO4 on seizures and sexual dimorphism, and the influence of circadian rhythms on seizure expression. ABSTRACT: Seizures are common in newborns after asphyxia at birth and are often refractory to anti-seizure agents. Magnesium sulphate (MgSO4 ) has anticonvulsant effects and is increasingly given to women in preterm labour for potential neuroprotection. There is limited information on its effects on perinatal seizures. We examined the hypothesis that MgSO4 infusion would reduce fetal seizures after asphyxia in utero. Preterm fetal sheep at 0.7 gestation (104 days, term = 147 days) were given intravenous infusions of either saline (n = 14) or MgSO4 (n = 12, 160 mg bolus + 48 mg h-1 infusion over 48 h). Fetuses underwent umbilical cord occlusion (UCO) for 25 min, 24 h after the start of infusion. The start time for seizures did not differ between groups, but MgSO4 significantly reduced the total number of seizures (P < 0.001), peak seizure amplitude (P < 0.05) and seizure burden (P < 0.005). Within the saline-asphyxia group, male fetuses had significantly more seizures than females (P < 0.05). Within the MgSO4 -asphyxia group, although both sexes had fewer seizures than the saline-asphyxia group, the greatest effect of MgSO4 was on male fetuses, with reduced numbers of seizures (P < 0.001) and seizure burden (P < 0.005). Only 1 out of 6 MgSO4 males had seizures on the second day post-UCO compared to 5 out of 6 MgSO4 female fetuses (P = 0.08). Finally, seizures showed a circadian profile with peak seizures between 04.00 and 06.00 h on the first and second day post-UCO. Collectively, these results suggest that MgSO4 may have utility in treating perinatal seizures and has sexually dimorphic effects.

Using multilayer perceptron computation to discover ideal insect olfactory receptor combinations in the mosquito and fruit fly for an efficient electronic nose.

The model organism, Drosophila melanogaster, and the mosquito Anopheles gambiae use 60 and 79 odorant receptors, respectively, to sense their olfactory world. However, a commercial "electronic nose" in the form of an insect olfactory biosensor demands very low numbers of receptors at its front end of detection due to the difficulties of receptor/sensor integration and functionalization. In this letter, we demonstrate how computation via artificial neural networks (ANNs), in the form of multilayer perceptrons (MLPs), can be successfully incorporated as the signal processing back end of the biosensor to drastically reduce the number of receptors to three while still retaining 100% performance of odorant detection to that of a full complement of receptors. In addition, we provide a detailed performance comparison between D. melanogaster and A. gambiae odorant receptors and demonstrate that A. gambiae receptors provide superior olfaction detection performance over D. melanogaster for very low receptor numbers. The results from this study present the possibility of using the computation of MLPs to discover ideal biological olfactory receptors for an olfactory biosensor device to provide maximum classification performance of unknown odorants.

Multilayer perceptron classification of unknown volatile chemicals from the firing rates of insect olfactory sensory neurons and its application to biosensor design.

In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

Extracellular ATP release predominantly mediates Ca2+ communication locally in highly organised, stellate-Like patterned networks of adult human astrocytes.

The 'Astrocyte Network' and the understanding of its communication has been posed as a new grand challenge to be investigated by contemporary science. However, communication studies in astrocyte networks have investigated traditional petri-dish in vitro culture models where cells are closely packed and can deviate from the stellate form observed in the brain. Using novel cell patterning approaches, highly organised, regular grid networks of astrocytes on chip, to single-cell fidelity are constructed, permitting a stellate-like in vitro network model to be realised. By stimulating the central cell with a single UV nanosecond laser pulse, the initiation/propagation pathways of stellate-like networks are re-explored. The authors investigate the mechanisms of intercellular Ca2+ communication and discover that stellate-like networks of adult human astrocytes in vitro actually exploit extracellular ATP release as their dominant propagation pathway to cells in the network locally; being observed even down to the nearest neighbour and next nearest neighbouring cells-contrary to the reported gap junction. This discovery has significant ramifications to many neurological conditions such as epilepsy, stroke and aggressive astrocytomas where gap junctions can be targeted. In cases where such gap junction targeting has failed, this new finding suggests that these conditions should be re-visited and the ATP transmission pathway targeted instead.

Eliciting calcium transients with UV nanosecond laser stimulation in adult patient-derived glioblastoma brain cancer cellsin vitro.

Objective.Glioblastoma (GBM) is the most common and lethal type of high-grade adult brain cancer. The World Health Organization have classed GBM as an incurable disease because standard treatments have yielded little improvement with life-expectancy being 6-15 months after diagnosis. Different approaches are now crucial to discover new knowledge about GBM communication/function in order to establish alternative therapies for such an aggressive adult brain cancer. Calcium (Ca2+) is a fundamental cell molecular messenger employed in GBM being involved in a wide dynamic range of cellular processes. Understanding how the movement of Ca2+behaves and modulates activity in GBM at the single-cell level is relatively unexplored but holds the potential to yield opportunities for new therapeutic strategies and approaches for cancer treatment.Approach.In this article we establish a spatially and temporally precise method for stimulating Ca2+transients in three patient-derived GBM cell-lines (FPW1, RN1, and RKI1) such that Ca2+communication can be studied from single-cell to larger network scales. We demonstrate that this is possible by administering a single optimized ultra-violet (UV) nanosecond laser pulse to trigger GBM Ca2+transients.Main results.We determine that 1.58µJµm-2is the optimal UV nanosecond laser pulse energy density necessary to elicit a single Ca2+transient in the GBM cell-lines whilst maintaining viability, functionality, the ability to be stimulated many times in an experiment, and to trigger further Ca2+communication in a larger network of GBM cells.Significance.Using adult patient-derived mesenchymal GBM brain cancer cell-lines, the most aggressive form of GBM cancer, this work is the first of its kind as it provides a new effective modality of which to stimulate GBM cells at the single-cell level in an accurate, repeatable, and reliable manner; and is a first step toward Ca2+communication in GBM brain cancer cells and their networks being more effectively studied.

UV Laser Stimulation of Ca2+ Transients in Aggressive Glioblastoma Brain Cancer Cells.

Glioblastoma (GBM) is a lethal astrocytoma being the most common highest-grade adult brain cancer. GBM tumours are highly invasive and display rapid growth to surrounding areas of the brain. Despite treatment, diagnosed patients continue to have poor prognosis with average survival time of 8 months. Calcium (Ca2+) is a main communication channel used in GBM and its understanding holds the potential to unlock new approaches to treatment. The aim of this work is to provide a first step to accurately evoking Ca2+ transients in GBM cells using single UV nanosecond laser pulses in vitro such that this communication pathway can be more reliably studied from the single-cell to the network level.

Patterning Networks of Grade IV Glioblastoma on Silicon Chip.

Glioblastoma (GBM) is the most aggressive high-grade brain cancer with a median survival time of <15 months. Due to GBMs fast and infiltrative growth patient prognosis is poor with recurrence after treatment common. Investigating GBMs ability to communicate, specifically via Ca2+ signaling, within its functional tumour networks may unlock new therapeutics to reduce the rapid infiltration and growth which currently makes treatment ineffective. This work aims to produce patterned networks of GBM cells such that the Ca2+ communication at a network level can be repeatedly and reliably investigated.

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks.

Anomaly detection is a significant task in sensors' signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors' applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded by three electrochemical aptasensors, with variations in the signals' lengths for particular concentrations, analytes, and bioreceptors. Prediction models employed autoencoder networks and the kernel density estimation (KDE) method for finding the threshold to detect anomalies. Moreover, the autoencoder networks were vanilla, unidirectional long short-term memory (ULSTM), and bidirectional LSTM (BLSTM) autoencoders for the training stage of the prediction models. However, the decision-making was based on the result of these three networks and the integration of vanilla and LSTM networks' results. The accuracy as a performance metric of anomaly prediction models showed that the performance of vanilla and integrated models were comparable, while the LSTM-based autoencoder models showed the least accuracy. Considering the integrated model of ULSTM and vanilla autoencoder, the accuracy for the dataset with the lengthier signals was approximately 80%, while it was 65% and 40% for the other datasets. The lowest accuracy belonged to the dataset with the least normal data in its dataset. These results demonstrate that the proposed vanilla and integrated models can automatically detect abnormal data when there is sufficient normal data for training the models.

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest.

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 µM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

Critical Spatial-Temporal Dynamics and Prominent Shape Collapse of Calcium Waves Observed in Human hNT Astrocytes in Vitro.

Networks of neurons are typically studied in the field of Criticality. However, the study of astrocyte networks in the brain has been recently lauded to be of equal importance to that of the neural networks. To date criticality assessments have only been performed on networks astrocytes from healthy rats, and astrocytes from cultured dissociated resections of intractable epilepsy. This work, for the first time, presents studies of the critical dynamics and shape collapse of calcium waves observed in cultures of healthy human astrocyte networks in vitro, derived from the human hNT cell line. In this article, we demonstrate that avalanches of spontaneous calcium waves display strong critical dynamics, including power-laws in both the size and duration distributions. In addition, the temporal profiles of avalanches displayed self-similarity, leading to shape collapse of the temporal profiles. These findings are significant as they suggest that cultured networks of healthy human hNT astrocytes self-organize to a critical point, implying that healthy astrocytic networks operate at a critical point to process and transmit information. Furthermore, this work can serve as a point of reference to which other astrocyte criticality studies can be compared.

Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training.

Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a 'Gradient Direction, Grey level Co-occurrence Matrix' (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.

Synthesis of encapsulated ZnO nanowires provide low impedance alternatives for microelectrodes.

Microelectrodes are commonly used in electrochemical analysis and biological sensing applications owing to their miniaturised dimensions. It is often desirable to improve the performance of microelectrodes by reducing their electrochemical impedance for increasing the signal-to-noise of the recorded signals. One successful route is to incorporate nanomaterials directly onto microelectrodes; however, it is essential that these fabrication routes are simple and repeatable. In this article, we demonstrate how to synthesise metal encapsulated ZnO nanowires (Cr/Au-ZnO NWs, Ti-ZnO NWs and Pt-ZnO NWs) to reduce the impedance of the microelectrodes. Electrochemical impedance modelling and characterisation of Cr/Au-ZnO NWs, Ti-ZnO NWs and Pt-ZnO NWs are carried out in conjunction with controls of planar Cr/Au and pristine ZnO NWs. It was found that the ZnO NW microelectrodes that were encapsulated with a 10 nm thin layer of Ti or Pt demonstrated the lowest electrochemical impedance of 400 ± 25 kΩ at 1 kHz. The Ti and Pt encapsulated ZnO NWs have the potential to offer an alternative microelectrode modality that could be attractive to electrochemical and biological sensing applications.

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks.

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor's performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals' lengths for specific concentrations. To find the optimal network, we altered the following variables: the LSTM layer structure (unidirectional LSTM (LSTM) and bidirectional LSTM (BLSTM)), optimizers (Adam, RMSPROP, SGDM), number of hidden units, and amount of augmented data. Then, the evaluation of the networks revealed that the highest original data accuracy increased from 50% to 92% by exploiting the data augmentation method. In addition, the SGDM optimizer showed a lower performance prediction than that of the ADAM and RMSPROP algorithms, and the number of hidden units was ineffective in improving the networks' performances. Moreover, the BLSTM nets showed more accurate predictions than those of the ULSTM nets on lengthier signals. These results demonstrate that this method can automatically detect the analyte concentration from the sensor signals.

Superior galvanostatic electrochemical deposition of platinum nanograss provides high performance planar microelectrodes forin vitroneural recording.

Objective.Platinum nanograss (Ptng) has been demonstrated as an excellent coating to increase the electrode roughness and reduce the impedance of microelectrodes for neural recording. However, the optimisation of the original potentiostatic electrochemical deposition (PSED) method has been performed by the original group only and noin vitrovalidation of functionality was reported.Approach.This study firstly reinvestigates the use of the PSED method for Ptng coating at different charge densities which highlights non-uniformities in the edges of the microelectrodes for increasing deposition charge densities, leading to a decreased impedance which is in fact an artefact. We then introduce a novel Ptng fabrication method of galvanostatic electrochemical deposition (GSED).Main results.We demonstrate that the GSED deposition method also significantly reduces the electrode impedance, raises the charge storage capacity and provides a significantly more planar electrode surface in comparison to the PSED method with negligible edge effects. In addition, we demonstrate how high-quality neural recordings were performed, for the first time, using the Ptng GSED deposition microelectrodes from human hNT neurons and how spiking and bursting were observed.Significance.Thus, the GSED Ptng deposition method presented here provides an alternative method of microelectrode fabrication for neural applications with excellent impedance and planarity of surface.

Geometric micro-shapes facilitate trackless connections between human astrocytes.

Objective.Cell patterning approaches commonly employed to direct the cytoplasmic outgrowth from cell bodies have been via chemical cues or biomaterial tracks. However, complex network designs using these approaches create problems where multiple tracks lead to manifold obstructions in design. A less common but alternative cell patterning modality is to geometrically design the nodes to project the cytoplasmic processes into a specific direction, thus, removing the need for tracks. Janget alperformed an in-depth study of how rodent neuron primaries could be directed accurately using geometric micro-shapes. In parallel and in contrast, to the work of Janget alwe investigate, for the first time, the effect that micro-shape geometry has on the cytoplasmic process outgrowth of human cells of astrocyte origin using the biomaterial parylene-C.Approach.We investigated eight different types of parylene-C micro-shape on SiO2substrates consisting of the: circle, square, pentagon, hexagon, equilateral triangle and three isosceles triangles with top vertex angles of 14.2°, 28.8°, and 97.6°, respectively. We quantified how each micro-shape influenced the: cell patterning, the directionality of the cytoplasmic process outgrowth and the functionality for human astrocyte.Main results.Human astrocytes became equally well patterned on all different micro-shapes. Human astrocytes could discriminate the underlying micro-shape geometry and preferentially extended processes from the vertices of equilateral triangles and isosceles triangles where the vertex angle equal to 28.8° in a repeatable manner whilst remaining functional.Significance.We demonstrate how human astrocytes are extremely effective at directing their cytoplasmic process outgrowth from the vertices of geometric micro-shapes, in particular the top vertex of triangular shapes. The significance of this work is that it demonstrates that geometric micro-shapes offer an alternative patterning modality to direct cytoplasmic process outgrowth for human astrocytes, which can serve to simplify complex network design, thus, removing the need for tracks.

Comparison of Leading Biosensor Technologies to Detect Changes in Human Endothelial Barrier Properties in Response to Pro-Inflammatory TNFα and IL1ß in Real-Time.

Electric Cell-Substrate Impedance Sensing (ECIS), xCELLigence and cellZscope are commercially available instruments that measure the impedance of cellular monolayers. Despite widespread use of these systems individually, direct comparisons between these platforms have not been published. To compare these instruments, the responses of human brain endothelial monolayers to TNFα and IL1ß were measured on all three platforms simultaneously. All instruments detected transient changes in impedance in response to the cytokines, although the response magnitude varied, with ECIS being the most sensitive. ECIS and cellZscope were also able to attribute responses to particular endothelial barrier components by modelling the multifrequency impedance data acquired by these instruments; in contrast the limited frequency xCELLigence data cannot be modelled. Consistent with its superior impedance sensing, ECIS exhibited a greater capacity than cellZscope to distinguish between subtle changes in modelled endothelial monolayer properties. The reduced resolving ability of the cellZscope platform may be due to its electrode configuration, which is necessary to allow access to the basolateral compartment, an important advantage of this instrument. Collectively, this work demonstrates that instruments must be carefully selected to ensure they are appropriate for the experimental questions being asked when assessing endothelial barrier properties.

Electroencephalogram studies of hypoxic ischemia in fetal and neonatal animal models.

Alongside clinical achievements, experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ischemic encephalopathy (HIE). Due to the reasonably fair degree of flexibility with experiments, most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury, following HI insult. This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents, piglets, sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram.

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Nanosecond Laser Stimulation in an Organized Grid Network of Human Astrocytes.

Recently, the study of communication in an 'Astrocyte Network' has been suggested to be of equal importance to that of the traditional 'Neural Network'. In this paper, for the first time, we use nanosecond laser stimulation to stimulate the central cell in an organized grid network of connected human astrocytes in order to observe calcium wave propagation at the single-cell level. We show that the calcium waves indeed propagate from the central astrocyte to the outer periphery of the organized astrocyte network. We observe also, like astrocytes in standard in vitro petri dishes, that the calcium wave propagates through specific connections to the outer periphery of cells rather than in a uniform radial manner predicted by mathematical theory. The results show that such a platform provides an excellent environment to perform repeatable, controlled studies of calcium wave signal propagation through an organized grid network of human astrocytes at single-cell resolution.