Structuring a multi-nodal neural network in vitro within a novel design microfluidic chip

Abstract In this study, we developed a new model of neuronal cells plating into a developed neural network to study functional integration using microfluidic methods. The integration was modeled in a three-chamber microfluidic chip by growing two weakly coupled neuronal networks and enhancing its connectivity by plating new dissociated cells. The direction of connections was formed by the asymmetric design of the chip. Such technology can be used to develop a new type of scaffold to recover the modular structure of the network.

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CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

Scientific Programming ◽

10.1155/2020/8847694 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Chuandong Song ◽

Haifeng Wang

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Classification Model ◽

Peptide Sequence ◽

Sequence Information ◽

Post Translational Modification ◽

Fuzzy Neural ◽

Human Complex ◽

Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

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A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽

10.1186/s13007-021-00714-9 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Mina Salehi ◽

Siamak Farhadi ◽

Ahmad Moieni ◽

Naser Safaie ◽

Mohsen Hesami

Keyword(s):

Neural Network ◽

Fruit Fly ◽

Corylus Avellana ◽

General Regression Neural Network ◽

Fruit Fly Optimization Algorithm ◽

Harvesting Time ◽

Fruit Fly Optimization ◽

General Regression ◽

Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

Scientific Reports ◽

10.1038/s41598-018-27858-4 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 16

Author(s):

Mohammad Mehdi Arab ◽

Abbas Yadollahi ◽

Maliheh Eftekhari ◽

Hamed Ahmadi ◽

Mohammad Akbari ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Culture Medium ◽

In Vitro Rooting ◽

Artificial Neural

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In Vitro Method for Predicting Glycemic Index of Foods Using Simulated Digestion and an Artificial Neural Network

Cereal Chemistry ◽

10.1094/cchem-87-4-0363 ◽

2010 ◽

Vol 87 (4) ◽

pp. 363-369 ◽

Cited By ~ 12

Author(s):

Robert L. Magaletta ◽

Suzanne N. DiCataldo ◽

Dong Liu ◽

Hong Laura Li ◽

Rajendra P. Borwankar ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Glycemic Index ◽

In Vitro Method ◽

Simulated Digestion ◽

Artificial Neural

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Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

10.1101/244442 ◽

2018 ◽

Cited By ~ 1

Author(s):

Zeinab Golgooni ◽

Sara Mirsadeghi ◽

Mahdieh Soleymani Baghshah ◽

Pedram Ataee ◽

Hossein Baharvand ◽

...

Keyword(s):

Neural Network ◽

Stem Cells ◽

Deep Learning ◽

Pluripotent Stem Cells ◽

Field Potential ◽

Input Sequence ◽

Patient Specific ◽

Field Potentials ◽

Drug Induced

AbstractAimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.Methods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.ConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of “irregularity or abnormal waveforms” in an in vitro model of cardiotoxicity experiments.

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Homeostatic plasticity and external input shape neural network dynamics

10.1101/362152 ◽

2018 ◽

Cited By ~ 1

Author(s):

Johannes Zierenberg ◽

Jens Wilting ◽

Viola Priesemann

Keyword(s):

Neural Network ◽

Brain Area ◽

External Input ◽

Homeostatic Plasticity ◽

Dynamic State ◽

Network Topologies ◽

Neural Network Dynamics ◽

Spiking Activity

In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.

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Artificial Neural Network in Drug Delivery and Pharmaceutical Research

The Open Bioinformatics Journal ◽

10.2174/1875036201307010049 ◽

2013 ◽

Vol 7 (1) ◽

pp. 49-62 ◽

Cited By ~ 23

Author(s):

Vijaykumar Sutariya ◽

Anastasia Groshev ◽

Prabodh Sadana ◽

Deepak Bhatia ◽

Yashwant Pathak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Drug Delivery ◽

Pattern Recognition ◽

Analytical Data ◽

Pharmaceutical Research ◽

Artificial Neural ◽

The Brain

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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