scholarly journals Artificial Neural Network in Drug Delivery and Pharmaceutical Research

2013 ◽  
Vol 7 (1) ◽  
pp. 49-62 ◽  
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.

Novel Phospholipid-Based Labrasol Nanomicelles Loaded Flavonoids for Oral Delivery with Enhanced Penetration and Anti-Brain Tumor Efficiency

2020 ◽  
Vol 17 (3) ◽  
pp. 229-245
Author(s):  
Gang Wang ◽  
Junjie Wang ◽  
Rui Guan
Keyword(s):  
Drug Delivery ◽  
Brain Tumor ◽  
Oral Delivery ◽  
Safe Delivery ◽  
Drug Delivery Vehicles ◽  
Therapeutic Benefits ◽  
Delivery Vehicles ◽  
The Brain

Background: Owing to the rich anticancer properties of flavonoids, there is a need for their incorporation into drug delivery vehicles like nanomicelles for safe delivery of the drug into the brain tumor microenvironment. Objective: This study, therefore, aimed to prepare the phospholipid-based Labrasol/Pluronic F68 modified nano micelles loaded with flavonoids (Nano-flavonoids) for the delivery of the drug to the target brain tumor. Methods: Myricetin, quercetin and fisetin were selected as the initial drugs to evaluate the biodistribution and acute toxicity of the drug delivery vehicles in rats with implanted C6 glioma tumors after oral administration, while the uptake, retention, release in human intestinal Caco-2 cells and the effect on the brain endothelial barrier were investigated in Human Brain Microvascular Endothelial Cells (HBMECs). Results: The results demonstrated that nano-flavonoids loaded with myricetin showed more evenly distributed targeting tissues and enhanced anti-tumor efficiency in vivo without significant cytotoxicity to Caco-2 cells and alteration in the Trans Epithelial Electric Resistance (TEER). There was no pathological evidence of renal, hepatic or other organs dysfunction after the administration of nanoflavonoids, which showed no significant influence on cytotoxicity to Caco-2 cells. Conclusion: In conclusion, Labrasol/F68-NMs loaded with MYR and quercetin could enhance antiglioma effect in vitro and in vivo, which may be better tools for medical therapy, while the pharmacokinetics and pharmacodynamics of nano-flavonoids may ensure optimal therapeutic benefits.

Neural networks and Keras

2019 ◽  
pp. 66-90
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Basic Operation ◽  
Neuron Models ◽  
Recent Success ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and how these operations are abstracted by simple neuron models. It then builds networks of artificial neurons that constitute much of the recent success of AI. The focus of this chapter is on using such techniques, with subsequent consideration of their theoretical embedding.

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

scholarly journals Encapsulated Escitalopram and Paroxetine Intranasal Co-Administration: In Vitro/In Vivo Evaluation

2021 ◽  
Vol 12 ◽  
Author(s):  
Soraia Silva ◽  
Joana Bicker ◽  
Carla Fonseca ◽  
Nuno R. Ferreira ◽  
Carla Vitorino ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Common Mental Disorder ◽  
Drug Loading ◽  
Pharmacokinetic Studies ◽  
Brain Delivery ◽  
In Vivo Evaluation ◽  
The Brain

Depression is a common mental disorder. Its treatment with selective serotonin reuptake inhibitors (SSRIs) is effective only in a fraction of patients, and pharmacoresistance is increasing steadily. Intranasal (IN) drug delivery to the brain stands out as a promising strategy to improve current therapeutic approaches by operating as a shuttle to overcome the blood–brain barrier. This work aimed to simultaneously administer escitalopram and paroxetine by IN route to mice. For this purpose, three nanostructured lipid carriers (NLC1, NLC2, and BorNLC) and one nanoemulsion (NE) were tested for drug loading. After their characterization, investigation of their impact on nasal cell viability and SSRI permeability assays were performed, using a human nasal RPMI 2650 cell line in air–liquid interface. In vitro assays demonstrated that NLCs, including borneol (BorNLC), significantly increased escitalopram permeability (p < 0.01) and paroxetine recovery values (p < 0.05) in relation to the other formulations and non-encapsulated drugs. IN and intravenous (IV) pharmacokinetic studies performed in vivo with a single dose of 2.38 mg/kg demonstrated similar results for escitalopram brain-to-plasma ratios. IN administrations delayed escitalopram peak concentrations in the brain for 15–60 min and no direct nose-to-brain delivery was detected. However, encapsulation with BorNLC considerably decreased escitalopram exposure in the lungs (124 μg min/g) compared with free escitalopram by IN (168 μg min/g) and IV (321 μg min/g) routes. Surprisingly, BorNLC IN instillation increased concentration levels of paroxetine in the brain by five times and accelerated brain drug delivery. Once again, lung exposure was considerably lower with BorNLC (AUCt = 0.433 μg min/g) than that with IV administration (AUCt = 1.01 μg min/g) and non-encapsulated IN formulation (AUCt = 2.82 μg min/g). Direct nose-to-brain delivery was observed for paroxetine IN administration with a direct transport percentage (DTP) of 56.9%. If encapsulated, it increases to 74.2%. These results clearly emphasize that nose-to-brain delivery and lung exposure depend on the formulation and on the characteristics of the drug under investigation. NLCs seem to be an advantageous strategy for nose-to-brain delivery of lipophilic molecules, since they reduce systemic and lung exposure, thereby decreasing adverse effects. For hydrophilic compounds, NLCs are particularly important to decrease lung exposure after IN administration.

scholarly journals Formation of neural networks with structural and functional features consistent with small-world network topology on surface-grafted polymer particles

2019 ◽  
Vol 6 (10) ◽  
pp. 191086 ◽  
Author(s):  
Vibeke Devold Valderhaug ◽  
Wilhelm Robert Glomm ◽  
Eugenia Mariana Sandru ◽  
Masahiro Yasuda ◽  
Axel Sandvig ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Topology ◽  
Small World ◽  
Network Activity ◽  
Small World Network ◽  
Grafted Polymer ◽  
Functional Features ◽  
The Brain

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure–function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology.

scholarly journals A Study of Connectionism Theory

2020 ◽  
Vol 3 (3) ◽  
pp. 2335-2342
Author(s):  
Shahryar Banan ◽  
Muhammad Ridwan ◽  
Abdurahman Adisaputera
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Neuroscience ◽  
Paradigm Shift ◽  
Interconnection Network ◽  
Mental Abilities ◽  
Artificial Neural ◽  
The Brain

The development of connectionism represents a paradigm shift in science. Connectionism has its root in cognitive and computational neuroscience. Likening the brain to a computer, connectionism tries to describe human mental abilities in terms of artificial neural networks. A neural network consists of a large number of nodes and units which are joined together to form an interconnection network. Within these interconnections, knowledge is distributed. Therefore learning is a processing by-product. This article is about the concept of connectionism, what it accounts for and what it doesn't take into account.  Finally, different approaches to connectionism are discussed.

scholarly journals FORMING EVOLUTIONARY DESIGN OF NEURAL NETWORKS WITH DIFFERENT NODES

2014 ◽  
pp. 16-23
Author(s):  
Eva Volna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Feedforward Neural Network ◽  
Evolutionary Design ◽  
Pattern Recognition Problem ◽  
Multilayer Feedforward Neural Network ◽  
Artificial Neural ◽  
Fully Connected

Evolution in artificial neural networks (e.g. neuroevolution) searches through the space of behaviours for a network that performs well at a given task. Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward neural network with fixed architecture. In this article, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network’s performance. In order to test the efficiency of described method, we applied it to the pattern recognition problem and to the alphabet coding problem.

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

2011 ◽  
pp. 2233-2252
Author(s):  
Ju¨rgen Perl ◽  
Peter Dauscher
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Complex Structure ◽  
Optimization Methods ◽  
Motor Activities ◽  
Artificial Neural ◽  
Basic Ideas ◽  
Network Approaches

Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural methods can be used, especially in the field of sport.

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