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.

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.

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.

UV Laser Stimulation of Ca2+ Transients in Aggressive Glioblastoma Brain Cancer Cells. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083047 DOI: 10.1109/embc40787.2023.10341039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083627 DOI: 10.1109/embc40787.2023.10340936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

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.

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.

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.

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.

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.

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.

Deep Convolutional Neural Networks for the Accurate Identification of High-Amplitude Stereotypic Epileptiform Seizures in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep

Neonatal seizures after birth may contribute to brain injury after an hypoxic-ischemic (HI) event, impaired brain development and a later life risk for epilepsy. Despite neural immaturity, seizures can also occur in preterm infants. However, surprisingly little is known about their evolution after an HI insult or patterns of expression. An improved understanding of preterm seizures will help facilitate diagnosis and prognosis and the implementation of treatments. This requires improved detection of seizures, including electrographic seizures. We have established a stable preterm fetal sheep model of HI that results in different types of post-HI seizures. These including the expression of epileptiform transients during the latent phase (0-6 h) of cerebral energy recovery, and bursts of high amplitude stereotypic evolving seizures (HAS) during the secondary phase of cerebral energy failure (∼6-72 h). We have previously developed successful automated machine-learning strategies for accurate identification and quantification of the evolving micro-scale EEG patterns (e.g. gamma spikes and sharp waves), during the latent phase. The current paper introduces, for the first time, a real-time approach that employs a 15-layer deep convolutional neural network (CNN) classifier, directly fed with the raw EEG time-series, to identify HAS in the 1024Hz and 256Hz down-sampled data in our preterm fetuses post-HI. The classifier was trained and tested using EEG segments during ∼6 to 48 hours post-HI recordings. The classifier accurately identified HAS with 98.52% accuracy in the 1024Hz and 97.78% in the 256Hz data. Clinical relevance-Results highlight the promising ability of the proposed CNN classifier for accurate identification of HI related seizures in the neonatal preterm brain, if further applied to the current 256Hz clinical recordings, in real-world.

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated that micro-scale patterns evolve over a profoundly suppressed EEG background within the first 6 hours of recovery, post HI insult. In particular, we have shown that high-frequency micro-scale spike transients (in the gamma frequency band, 80-120Hz) emerge immediately after an HI event, with much higher numbers around 2-2.5 h of the insult, with numbers gradually declining thereafter. We have also shown that the automatically quantified sharp waves in this phase are predictive of neural outcome. Initiation of some neuroprotective treatments within this limited window of opportunity, such as therapeutic hypothermia, optimally reduces neural injury. In clinical practice, it is hard to determine the exact timing of the injury, therefore, reliable automatic identification of EEG transients could be beneficial to help specify the phases of injury. Our team has previously developed successful machine- and deep-learning strategies for the identification of post-HI EEG patterns in an HI preterm fetal sheep model.This paper introduces, for the first time, a novel online fusion approach to train an 11-layers deep convolutional neural network (CNN) classifier using Wavelet-Fourier (WF) spectral features of EEG segments for accurate identification of high-frequency micro-scale spike transients in 1024Hz EEG recordings in our preterm fetal sheep. Sets of robust features were extracted using reverse biorthogonal wavelet (rbio2.8 at scale 7) and considering an 80-120Hz spectral frequency range. The WF-CNN classifier was able to accurately identify spike transients with a reliable high-performance of 99.03±0.86%.Clinical relevance-Results confirm the expertise of the method for the identification of similar patterns in the EEG of neonates in the early hours after birth.

A novel approach to segment cortical neurons in histological images of the near-term fetal sheep brain model

Oxygen deprivation (hypoxia) and reduced blood supply (ischemia) can occur before, during or shortly after birth and can result in death, brain damage and long-term disability. Assessing neuronal survival after hypoxia-ischemia in the near-term fetal sheep brain model is essential for the development of novel treatment strategies. As manual quantification of neurons in histological images varies between different assessors and is extremely time-consuming, automation of the process is needed and has not been currently achieved. To achieve automation, successfully segmenting the neurons from the background is very important. Due to presence of densely populated overlapping cells and with no prior information of shapes and sizes, the segmentation of neurons from the image is complex. Initially, we segmented the RGB images by using K-means clustering to primarily segment the neurons from the background based on their colour value, a distance transform for seed detection and watershed method for separating overlapping objects. However, this resulted in unsatisfactory sensitivity and performance due to over-segmentation if we use the RGB image directly. In this paper, we propose a semi-automated modified approach to segment neurons that tackles the over-segmentation issue that we encountered. Initially, we separated the red, green and blue colour channel information from the RGB image. We determined that by applying the same segmentation method first to the blue channel image, then by performing segmentation on the green channel for the neurons that remain unsegmented from the blue channel segmentation and finally by performing segmentation on red channel for neurons that were still unsegmented from the green channel segmentation, improved performance results could be achieved. The modified approach increased performance for the healthy and ischemic animal images from 89.7% to 98.08% and from 94.36% to 98.06% respectively as compared to using RGB image directly.

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.

The Effect of Basic Microshapes on hNT Astrocytes Cytoplasmic Process Outgrowth

Astrocytes are a non-homogeneous cell type, highly mobile which constantly extend and retract their cytoplasmic processes in what would seem random in direction. In this paper, we investigate how simple geometric microshapes can be used to control the outgrowth of human astrocytes cytoplasmic processes. We investigate the effect of how five regular microshapes: the circle, triangle, square, pentagon and hexagon control astrocyte cytoplasmic process outgrowth. For all the different microshape types, we observe that it is the corners of the shapes that that cause the astrocyte to produce spontaneous outgrowth except for the circle where the outgrowth occurs at a random radial position. This work suggests that the geometry of cell adhesive regions effects the outgrowth of hNT astrocytes.

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.

Automatically Identified Micro-scale Sharp-wave Transients in the Early-Latent Phase of Hypoxic-Ischemic EEG from Preterm Fetal Sheep Reveal Timing Relationship to Subcortical Neuronal Survival

Perinatal Hypoxic-Ischemia Encephalopathy (HIE) in newborn infants, due to birth-related circumstances such as oxygen deprivation in brain cells, is caused by the disruption in blood flow through the umbilical cord. Subcortical neuronal loss due to the HIE can lead to cerebral palsy and other chronic neurological conditions. Pre-clinical EEG studies using in utero sheep have demonstrated that particular micro-scale HI transients emerge along a suppressed EEG background during a latent phase of 3-6 hours, after a severe HI insult. Whilst the nature of these micro-scale transients is not well understood, it has been hypothesized that such transients may be signatures of the evolving hypoxic-ischemic brain injury, possessing the potential to be served as the diagnosis biomarkers for the injury. Cerebral hypothermia is optimally neuroprotective only if administered within the first 2-3 hours post HI insult. Using data from a cohort of in utero preterm fetal sheep (n=5, at 0.7 of gestational age), this paper indicates how the number of automatically quantified micro-scale sharp wave transients from asphyxiated preterm fetal sheep, statistically correlate to the amount of NeuN-positive neurons measured in caudate nucleus of striatum. Different temporal window sizes of 2hrs, 1hr, ½hr and 10mins within the early phase of the latent phase are examined using our developed Wavelet Type-2 Fuzzy classifier for sharp detection. Analyses were narrowed down to 10min intervals to assess where exactly in time the occurrence of the HI micro-scale sharp waves demonstrate a significant correlation. Signal processing wise, results from the sub-windows indicate a timing trend that highlights a positive correlation, between the number of automatic quantifications and the amount of surviving neurons in the preterm brain, permitting the possibility of a point of care (POC) intervention to stop the spread of injury before it becomes irreversible.

2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep

We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable prognostic biomarkers for Hypoxic-Ischemic Encephalopathy (HIE). Higher number of sharp waves within the first 2 hours from a hypoxic insult is shown to be significantly correlated to subcortical neuronal survival in caudate nucleus of striatum. Cerebral therapeutic hypothermia is also shown to be optimally neuroprotective only if initiated as soon as possible during a short window of opportunity within the first 2-3 hours of HI insult, called the latent phase. Therefore there is an urgent necessity for reliable automated algorithms to robustly identify such biomarkers to help early diagnosis of HIE, in real time at birth, before the optimal window of opportunity for treatment is missed.We have previously introduced successful automated signal processing strategies based on the fusion of wavelet and fuzzy techniques, for real-time identification and quantification of sharp waves along a profoundly suppressed EEG/ECoG background, post HI-insult, during the latent phase of sheep models. This work, in particular, for the first time represents a novel online fusion strategy based on the combination of a deep Convolutional Neural Network (CNN) in conjunction with Wavelet Scalogram (WS) for the real-time identification and classification of micro-scale sharp wave biomarkers within the 1024Hz high resolution ECoG recordings as well as the down-sampled 256Hz signals, from in utero preterm fetal sheep. The WS-CNN classifier highlights ability in the identification of HI sharp waves with remarkable high accuracies of 95.34% for 1024Hz and 94.62% for 256Hz data tested over one hour HI ECoG within the most important interval during the first 2 hours of the latent phase, where experiments have suggested hypothermia is optimally effective.

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.

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.

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.

Evaluation of parylene derivatives for use as biomaterials for human astrocyte cell patterning

Cell patterning is becoming increasingly popular in neuroscience because it allows for the control in the location and connectivity of cells. A recently developed cell patterning technology uses patterns of an organic polymer, parylene-C, on a background of SiO₂. When cells are cultured on the parylene-C/SiO₂ substrate they conform to the underlying parylene-C geometry. Parylene-C is, however, just one member of a family of parylene polymers that have varying chemical and physical properties. In this work, we investigate whether two commercially available mainstream parylene derivatives, parylene-D, parylene-N and a more recent parylene derivative, parylene-HT to determine if they enable higher fidelity hNT astrocyte cell patterning compared to parylene-C. We demonstrate that all parylene derivatives are compatible with the existing laser fabrication method. We then demonstrate that parylene-HT, parylene-D and parylene-N are suitable for use as an hNT astrocyte cell attractive substrate and result in an equal quality of patterning compared to parylene-C. This work supports the use of alternative parylene derivatives for applications where their different physical and chemical properties are more suitable.

Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier

Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from in utero fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6-7h post-insult. However, having to observe for the whole of the latent phase disqualifies any chance of clinical intervention. A precise automatic identification of these transients can help for a well-timed diagnosis of the HIE and to stop the spread of the injury before it becomes irreversible. This paper reports fusion of Reverse-Biorthogonal Wavelets with Type-1 Fuzzy classifiers, for the accurate real-time automatic identification and quantification of high-frequency HI spike transients in the latent phase, tested over seven in utero preterm sheep. Considerable high performance of 99.78 ± 0.10% was obtained from the Rbio-Wavelet Type-1 Fuzzy classifier for automatic identification of HI spikes tested over 42h of high-resolution recordings (sampling-freq:1024Hz). Data from post-insult automatic time-localization of high-frequency HI spikes reveals a promising trend in the average rate of the HI spikes, even in the animals with shorter occlusion periods, which highlights considerable higher number of transients within the first 2h post-insult.

EEG sharp waves are a biomarker of striatal neuronal survival after hypoxia-ischemia in preterm fetal sheep

The timing of hypoxia-ischemia (HI) in preterm infants is often uncertain and there are few biomarkers to determine whether infants are in a treatable stage of injury. We evaluated whether epileptiform sharp waves recorded from the parietal cortex could provide early prediction of neuronal loss after HI. Preterm fetal sheep (0.7 gestation) underwent acute HI induced by complete umbilical cord occlusion for 25 minutes (n = 6) or sham occlusion (control, n = 6). Neuronal survival was assessed 7 days after HI by immunohistochemistry. Sharp waves were quantified manually and using a wavelet-type-2-fuzzy-logic-system during the first 4 hours of recovery. HI resulted in significant subcortical neuronal loss. Sharp waves counted by the automated classifier in the first 30 minutes after HI were associated with greater neuronal survival in the caudate nucleus (r = 0.80), whereas sharp waves between 2–4 hours after HI were associated with reduced neuronal survival (r = −0.83). Manual and automated counts were closely correlated. This study suggests that automated quantification of sharp waves may be useful for early assessment of HI injury in preterm infants. However, the pattern of evolution of sharp waves after HI was markedly affected by the severity of neuronal loss, and therefore early, continuous monitoring is essential.

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.

Patterning of functional human astrocytes onto parylene-C/SiO2 substrates for the study of Ca2+ dynamics in astrocytic networks

OBJECTIVE

Recent literature suggests that astrocytes form organized functional networks and communicate through transient changes in cytosolic Ca²⁺. Traditional techniques to investigate network activity, such as pharmacological blocking or genetic knockout, are difficult to restrict to individual cells. The objective of this work is to develop cell-patterning techniques to physically manipulate astrocytic interactions to enable the study of Ca²⁺ in astrocytic networks.

APPROACH

We investigate how an in vitro cell-patterning platform that utilizes geometric patterns of parylene-C on SiO₂ can be used to physically isolate single astrocytes and small astrocytic networks.

MAIN RESULTS

We report that single astrocytes are effectively isolated on 75  ×  75 µm square parylene nodes, whereas multi-cellular astrocytic networks are isolated on larger nodes, with the mean number of astrocytes per cluster increasing as a function of node size. Additionally, we report that astrocytes in small multi-cellular clusters exhibit spatio-temporal clustering of Ca²⁺ transients. Finally, we report that the frequency and regularity of Ca²⁺ transients was positively correlated with astrocyte connectivity.

SIGNIFICANCE

The significance of this work is to demonstrate how patterning hNT astrocytes replicates spatio-temporal clustering of Ca²⁺ signalling that is observed in vivo but not in dissociated in vitro cultures. We therefore highlight the importance of the structure of astrocytic networks in determining ensemble Ca²⁺ behaviour.

Examining the effect of MgSO4 on sharp wave transient activity in the hypoxic-ischemic fetal sheep model

Hypoxic-ischemic encephalopathy (HIE) due to lack of oxygen is a debilitating disorder experienced by a significant number of preterm infants during birth. Studies show that the brain undergoes different phases of injury following hypoxic insult, but the first 6-8 hours (known as a latent phase) are the key to treatment efficacy. Cerebral hypothermia is one known treatment, and for it to be effective it must be started during the latent phase and continued for several days. In order to determine the effectiveness of treatment it is important to pinpoint the time of insult. Monitoring of sharp wave transient activity in the hypoxic-ischemic (HI) electroencephalogram (EEG) could be a predictor for time of hypoxic insult. Due to practicality, it is optimal if this monitoring is performed automatically. Further, MgSO4 is a drug given to an increasing number of women in labor, due to its neuroprotective properties. This drug may influence transient activity in the HI fetal sheep EEG, leading to further complications in predicting hypoxic insult. This paper explores the effect of MgSO4 on sharp wave transient activity in the EEG of a HI fetal sheep. Demonstrated in this paper is the usage of a Wavelet-Type-II Fuzzy classifier to detect sharp wave transients during the latent phase of a control group fetal sheep and an MgSO4-treated fetal sheep. This detection was performed with an average overall performance of 93.21%±5.49 over 660 minutes of latent phase, post occlusion. There were no significant differences in number of sharp wave transients in the early- and mid-latent phases of injury for both fetal sheep. However, in the late-latent phase the MgSO4-treated fetal sheep had significantly fewer sharp wave transients than the control fetal sheep.

Improving odorant chemical class prediction with multi-layer perceptrons using temporal odorant spike responses from drosophila melanogaster olfactory receptor neurons

In this work, we examine the possibility of improving the prediction performance of an olfactory biosensor through the use of temporal spiking data. We present an Artificial Neural Network (ANN), in the form of an optimal hybrid Multi-Layer Perceptron (MLP) system for the classification of chemical odorants from olfactory receptor neuron spike responses of the Drosophila melanogaster fruit fly (DmOrs). The data used in this study contains the responses to 34 odorants from 6 individual DmOrs, of which we exploit the temporal spiking responses of a 500ms odorant stimulus window. We report, for the first time, the difference between the classification performance of the temporal spiking data to an equivalent spontaneous scalar dataset that we have reported previously. We demonstrate that a higher prediction (%) was obtained when using the temporal data, in which a greater number of validation odorants are identified to their correct chemical class. This work presents a novel technique to improve the classification performance of an olfactory biosensor, whilst maintaining a limited sensory array of 6 DmOr receptors.

Investigation of the Ca2+ response of human hNT astrocytes to laser removal of cellular processes

We demonstrate, for the first time, UV laser ablative microsurgery as a method for pruning astrocytic processes from live hNT astrocytic networks in vitro. Calcium fluorescence imaging was used to evaluate the cellular response to process ablation. The results showed that ablation of astrocyte processes induced an immediate increase in intracellular calcium level which propagated through the cells cytoplasm as a wave originating from the ablation site. The increased intracellular calcium dissipated from the body of the cell but remained high in the vicinity of the ablation site. Cell viability post ablation was confirmed by observing the integrity of the cell membrane. Ablation of astrocytic processes did not compromise cell viability whereas ablation of the cytoplasm using the same laser energy resulted in cell lysis.

"Super e-noses": Multi-layer perceptron classification of volatile odorants from the firing rates of cross-species olfactory receptor arrays

Current electronic noses, or e-noses, that employ insect odorant receptors (Ors) as their sensory front end are potentially limited by the fact that the Ors come from a single species. In addition, a realistic e-nose also demands low numbers of Ors at its sensory front end due to the difficulties of receptor/sensor integration and functionalisation. In this work, we report the first investigations of a `Super E-Nose' that incorporates Ors from both the model organism Drosophila melanogaster fruit fly (DmOr) and the mosquito, Anopheles gambiae (AgOr). Furthermore, we report how an Artificial Neural Network (ANN), in the form of a hybrid double hidden layer Multi-Layer Perceptron (MLP), can be used to determine the optimal Ors that provide the best prediction performance in the classification of unknown odorants into their respective chemical class. Our findings demonstrate how 3-Or arrays consisting of DmOr only, AgOr only, or cross-species DmOr-AgOr combinations correctly classified all unknown odorants of the validation set. In addition, we report that all 3-Or combinations perform equally well as the complete 74 DmOr-AgOr array. Thus, the results of this work support further investigation into cross-species `Super E-noses' coupled with hybrid MLPs for the classification of unknown odorants.

Using type-2 fuzzy logic systems for spike detection in the hypoxic ischemic EEG of the preterm fetal sheep

Perinatal hypoxia is a major cause of brain injury in preterm babies. Thus, neuro-protective treatments play a pivotal role during the first 6-8 hours post hypoxic-ischemic insult. However, at present it is not possible to determine which infants are suffering from hypoxic ischemia. Recent investigations suggest that there are high frequency micro-scale transients exist in the first 6-8 hours of a hypoxic ischemic EEG which could be utilized as the useful benchmarks for the prediction of hypoxia. Type-2 Fuzzy Logic Systems (Type-2 FLS) have the capability to handle inherent uncertainties in nonlinear signals. This paper describes the application of a Type-2 FLS to detect spikes in the preterm fetal sheep electroencephalogram (EEG) after asphyxia in utero. The Type-2 FLS differentiates each detected event in terms of its spikiness and specifies the potential events based on their degree of similarity to an EEG expert definition of a standard spike. An adaptive thresholding method has been employed in order to increase the spike detection ability of the purposed system. The sensitivity and selectivity verify enhanced performance of the Type-2 FLS for spike detection in fetal sheep EEG signals with a 98.1% and 93.7% respectively which are significantly improved in comparison to our previous methods.

Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.

Investigating parylene-HT as a substrate for human cell patterning

We demonstrate, for the first time, how parylene-HT on SiO2 substrates can be used as a human cell patterning platform. We demonstrate this platform with hNT astrocytes, derived from the human NTera2.D1 cell line. We show how hNT astrocytes are attracted to Parylene-HT and repelled by the SiO2 and are shown to adopt a similar morphology as that attained on standard tissue culture polystyrene. Furthermore, parylene-HT was capable of patterning the astrocytes achieving a ratio of 8:1 for cells on parylene compared to SiO2. Thus, as parylene-HT has similar physical properties to parylene-C with the addition of UV and thermal resistance, parylene-HT represents a desirable alternative substrate for human cell patterning.

Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier

Perinatal hypoxic-ischemic encephalopathy (HIE) around the time of birth due to lack of oxygen can lead to debilitating neurological conditions such as epilepsy and cerebral palsy. Experimental data have shown that brain injury evolves over time, but during the first 6-8 hours after HIE the brain has recovered oxidative metabolism in a latent phase, and brain injury is reversible. Treatments such as therapeutic cerebral hypothermia (brain cooling) are effective when started during the latent phase, and continued for several days. Effectiveness of hypothermia is lost if started after the latent phase. Post occlusion monitoring of particular micro-scale transients in the hypoxic-ischemic (HI) Electroencephalogram (EEG), from an asphyxiated fetal sheep model in utero, could provide precursory evidence to identify potential biomarkers of injury when brain damage is still treatable. In our studies, we have reported how it is possible to automatically detect HI EEG transients in the form of spikes and sharp waves during the latent phase of the HI EEG of the preterm fetal sheep. This paper describes how to identify stereotypic evolving micro-scale seizures (SEMS) which have a relatively abrupt onset and termination in a frequency range of 1.8-3Hz (Delta waves) superimposed on a suppressed EEG amplitude background post occlusion. This research demonstrates how a Wavelet Type-II Fuzzy Logic System (WT-Type-II-FLS) can be used to automatically identify subtle abnormal SEMS that occur during the latent phase with a preliminary average validation overall performance of 78.71%±6.63 over the 390 minutes of the latent phase, post insult, using in utero pre-term hypoxic fetal sheep models.

Superiority of high frequency hypoxic ischemic EEG signals of fetal sheep for sharp wave detection using Wavelet-Type 2 Fuzzy classifiers

There is approximately a 6-8 hour window that exists from when a hypoxic-ischemic insult occurs, in utero, before significant irreversible brain injury occurs in new born infants. The focus of our work is to determine through the electroencephalogram (EEG) if such a hypoxic-ischemic insult has occurred such that neuro-protective treatment can be sought within this period. At present, there are no defined biomarkers in the EEG that are currently being used to help classify if a hypoxic ischemia insult has occurred. However, micro-scale transients in the form of spikes, sharps and slow waves exists that could provide precursory information whether a hypoxic-ischemic insult has occurred or not. In our previous studies we have successfully automatically identified spikes with high sensitivity and selectivity in the conventional 64Hz sampled EEG. This paper details the significant advantage that can be obtained in using high frequency 1024Hz sampled EEG for sharp wave detection over the typically employed 64Hz sampled EEG. This advantage is amplified when a combination of wavelet Type-2 Fuzzy Logic System (WT-Type-2-FLS) classifiers are used to identify the sharp wave transients. By applying WT-Type-2-FLS to the 1024Hz EEG record and to the same down-sampled 64Hz EEG record we demonstrate, how the sharp wave transients detection increases significantly for high resolution 1024Hz EEG over 64Hz EEG. The WT-Type-2-FLS algorithm performance was assessed over 3 standardised time periods within the first 8 hours, post occlusion of a fetal sheep, in utero. 1024Hz EEG results demonstrate the algorithm detected sharps with overall performance rates of 85%, 92%, and 87% in the Early/Mid and Late-latent phases of injury, respectively as compared to 25%, 55% and 31% in the 64Hz EEG. These results demonstrate the power of Wavelet Type-2 Fuzzy Logic System at detecting sharp waves in 1024Hz EEG and suggest that there should be a movement toward recording high frequency EEG for analysis of hypoxic ischemic micro-scale transients that does not occur at present.

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.

Artificial neural network prediction of specific VOCs and blended VOCs for various concentrations from the olfactory receptor firing rates of Drosophila melanogaster

In our previous work, we have investigated the classification of odorants based on their chemical classes only, e.g. Alcohol, Terpene or Ester, using Artificial Neural Networks (ANN) as the signal processing backend of an insect olfactory electronic nose, or e-nose. However, potential applications of e-noses in the food and beverage industry which include the assessment of a fruit's ripeness, quality of wines or identifying bacterial contamination in products, demand the ability to predict beyond chemical class and to identify exact chemicals, known as specific Volatile Organic Compounds (VOCs) and blends of chemical that present themselves as aromas, known as blended VOCs (BVOCs). In this work, we demonstrate for the first time how it is possible to predict such VOCs and also BVOCs at varying concentration levels. We achieve this goal by using ANNs in the form of hybrid Multi-Layer Perceptrons (MLPs), to analyze the firing rate responses of the model organism Drosophila melanogaster's odorant receptors (DmOrs), in order to predict the specific VOCs and BVOCs. We report for the raw and noise injected data how the highest MLP prediction for specific VOCs occurred at a 10(-4)mol.dm(-3) concentration in which all the VOC validation vectors were identified and at a concentration of 10(-2)mol.dm(-3) for BVOCs in which 8/9 or 88.9% were identified. The results demonstrate for the first time the power of using MLPs and insect odorant receptors (Ors) to predict specific VOCs and BVOCs.

Application of artificial neural networks on mosquito Olfactory Receptor Neurons for an olfactory biosensor

Various odorants such as carbon dioxide (CO2) and 1-octen-3-ol, underlie the host-seeking behaviors of the major malaria vector Anopheles Gambiae. Highlighted by the olfactory processing strength of the mosquito, such a powerful olfactory sense could serve as the sensors of an artificial olfactory biosensor. In this work, we use the firing rates of the A. Gambiae mosquito Olfactory Receptor Neurons (ORNs), to train an Artificial Neural Network (ANN) for the classification of volatile odorants into their known chemical classes and assess their suitability for an olfactory biosensor. With the implementation of bootstrapping, a more representative result was obtained wherein we demonstrate the training of a hybrid ANN consisting of an array of Multi-Layer Perceptrons (MLPs) with optimal number of hidden neurons. The ANN system was able to correctly class 90.1% of the previously unseen odorants, thus demonstrating very strong evidence for the use of A. Gambiae olfactory receptors coupled with an ANN as an olfactory biosensor.

Fast detection & modelling of the real osteoarthritic holes in the human knee with contour interpolated radial basis functions

In this article, we propose a novel method for the fast 3D reconstruction of real osteoarthritic (OA) holes in a human femoral cartilage. Initially, semi-automated Region-Based Segmentation (region-growing) and Bounding Box techniques are used to extract femoral cartilage slices from MRI scans of the knee. OA holes were detected and filled automatically by our contour interpolation/RBF (CI-RBF) method and 3D models of both the femoral cartilage and OA holes were reconstructed separately. The method was then applied to a single human knee and results proved it fast, reliable and accurate for reconstructing a 3D model of the femoral cartilage from MRI images with an extremely low root mean square error of 1.67% in the estimated volume of the automatically filled to the manually filled femoral cartilage slices. As per authors' knowledge this is the first time real OA hole has automatically been identified and filled.

Reverse bi-orthogonal wavelets & fuzzy classifiers for the automatic detection of spike waves in the EEG of the hypoxic ischemic pre-term fetal sheep

There exists a 6-8 hour window of opportunity for the treatment of perinatal Hypoxic-Ischemic Encephalopathy (HIE) following the original insult after which significant irreversible brain injury manifests leading to debilitating neurological conditions such as epilepsy and cerebral palsy. At present, there are no identified biomarkers in the electroencephalogram (EEG) that are currently being used to help classify if a HIE insult has occurred or not. However, high frequency micro-scale transients in the form of spikes, sharp waves and slow waves appear in the EEG, post insult, that could provide precursory information whether a HIE insult has occurred or not. This paper describes the superiority of using reverse bi-orthogonal wavelets (RBIO-WT), in the form of the rbio2.8 mother wavelet, in conjunction with a Type-1 Fuzzy Logic System (Type-I FLS) classifier for accurate micro-scale spike wave transient detection in the EEG of Pre-term Fetal Sheep. The algorithm performance for spike detection was assessed over the most critical time period of 25 minutes within the first 8 hours, post occlusion using an in utero fetal sheep model. Obtained results demonstrate that the suggested algorithm detected spikes with a considerably high overall performance of 99.25% using the developed RBIO-WT Type-I FLS.

Enrichment of differentiated hNT neurons and subsequent analysis using flow-cytometry and xCELLigence sensing

BACKGROUND

Human neurons (hNT neurons), obtained from the NTera2/D1 precursor cell line, are highly valued by many neuroscientists as isolation of adult human primary neuronal cells continues to elude us. hNT neurons are generated by differentiation of the NT2 precursors for a period of 4 weeks followed by 2 weeks of mitotic inhibition. This yields a heterogeneous population of neuronal phenotypes and underlying astrocyte precursors, the latter of which are very difficult to visualise using standard light microscopy. Such a mixed culture is acceptable for some applications (e.g. measurement of synaptic plasticity), whereas others (e.g. proteomics or transcriptomics) require almost pure cultures of hNT neurons.

NEW METHOD

Here we describe a simple method for obtaining highly enriched cultures of hNT neurons following the first neuronal harvest and detail several additional methods, namely flow-cytometry and xCELLigence© biosensor technology, to rapidly and reliably determine the purity and viability of the cultures.

COMPARISON WITH EXISTING METHODS

This method of enrichment for the neurons is novel and advances the end user applications of the cells.

RESULTS

In addition, we apply the enrichment method to conduct analysis of cell-surface markers using flow-cytometry on the enriched neuronal cells. Furthermore, we apply this method to generate enriched neuronal cells on which we conduct analysis of cell-surface markers using flow-cytometry.

CONCLUSIONS

Collectively, this paper describes several new advances, which will create opportunities when using these cells and similar preparations, and provides the protocol for analysis of these cells using flow-cytometry and biosensor technology.

A Cell Derived Active Contour (CDAC) method for robust tracking in low frame rate, low contrast phase microscopy - an example: the human hNT astrocyte

The problem of automated segmenting and tracking of the outlines of cells in microscope images is the subject of active research. While great progress has been made on recognizing cells that are of high contrast and of predictable shape, many situations arise in practice where these properties do not exist and thus many interesting potential studies - such as the migration patterns of astrocytes to scratch wounds - have been relegated to being largely qualitative in nature. Here we analyse a select number of recent developments in this area, and offer an algorithm based on parametric active contours and formulated by taking into account cell movement dynamics. This Cell-Derived Active Contour (CDAC) method is compared with two state-of-the-art segmentation methods for phase-contrast microscopy. Specifically, we tackle a very difficult segmentation problem: human astrocytes that are very large, thin, and irregularly-shaped. We demonstrate quantitatively better results for CDAC as compared to similar segmentation methods, and we also demonstrate the reliable segmentation of qualitatively different data sets that were not possible using existing methods. We believe this new method will enable new and improved automatic cell migration and movement studies to be made.

Low cost, patterning of human hNT brain cells on parylene-C with UV & IR laser machining

This paper describes the use of 800nm femtosecond infrared (IR) and 248nm nanosecond ultraviolet (UV) laser radiation in performing ablative micromachining of parylene-C on SiO2 substrates for the patterning of human hNT astrocytes. Results are presented that support the validity of using IR laser ablative micromachining for patterning human hNT astrocytes cells while UV laser radiation produces photo-oxidation of the parylene-C and destroys cell patterning. The findings demonstrate how IR laser ablative micromachining of parylene-C on SiO2 substrates can offer a low cost, accessible alternative for rapid prototyping, high yield cell patterning.