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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.