scholarly journals Proliferation and Cluster Analysis of Neurons and Glial Cell Organization on Nanocolumnar TiN Substrates

2020 ◽  
Vol 21 (17) ◽  
pp. 6249
Author(s):  
Alice Abend ◽  
Chelsie Steele ◽  
Sabine Schmidt ◽  
Ronny Frank ◽  
Heinz-Georg Jahnke ◽  
...  
Keyword(s):  
Glial Cells ◽  
Indium Tin Oxide ◽  
Clustering Algorithm ◽  
Electrode Materials ◽  
Clustering Algorithms ◽  
Detection Sensitivity ◽  
Neural Function ◽  
Promising Alternative ◽  
Cell Organization

Biomaterials employed for neural stimulation, as well as brain/machine interfaces, offer great perspectives to combat neurodegenerative diseases, while application of lab-on-a-chip devices such as multielectrode arrays is a promising alternative to assess neural function in vitro. For bioelectronic monitoring, nanostructured microelectrodes are required, which exhibit an increased surface area where the detection sensitivity is not reduced by the self-impedance of the electrode. In our study, we investigated the interaction of neurons (SH-SY5Y) and glial cells (U-87 MG) with nanocolumnar titanium nitride (TiN) electrode materials in comparison to TiN with larger surface grains, gold, and indium tin oxide (ITO) substrates. Glial cells showed an enhanced proliferation on TiN materials; however, these cells spread evenly distributed over all the substrate surfaces. By contrast, neurons proliferated fastest on nanocolumnar TiN and formed large cell agglomerations. We implemented a radial autocorrelation function of cellular positions combined with various clustering algorithms. These combined analyses allowed us to quantify the largest cluster on nanocolumnar TiN; however, on ITO and gold, neurons spread more homogeneously across the substrates. As SH-SY5Y cells tend to grow in clusters under physiologic conditions, our study proves nanocolumnar TiN as a potential bioactive material candidate for the application of microelectrodes in contact with neurons. To this end, the employed K-means clustering algorithm together with radial autocorrelation analysis is a valuable tool to quantify cell-surface interaction and cell organization to evaluate biomaterials’ performance in vitro.

scholarly journals Adhesion of Neurons and Glial Cells with Nanocolumnar TiN Films for Brain-Machine Interfaces

2021 ◽  
Vol 22 (16) ◽  
pp. 8588
Author(s):  
Alice Abend ◽  
Chelsie Steele ◽  
Heinz-Georg Jahnke ◽  
Mareike Zink
Keyword(s):  
Glial Cells ◽  
Biomedical Applications ◽  
Electrode Materials ◽  
Detection Sensitivity ◽  
In Vitro Assays ◽  
Electric Signal ◽  
Multielectrode Arrays ◽  
Underlying Substrate

Coupling of cells to biomaterials is a prerequisite for most biomedical applications; e.g., neuroelectrodes can only stimulate brain tissue in vivo if the electric signal is transferred to neurons attached to the electrodes’ surface. Besides, cell survival in vitro also depends on the interaction of cells with the underlying substrate materials; in vitro assays such as multielectrode arrays determine cellular behavior by electrical coupling to the adherent cells. In our study, we investigated the interaction of neurons and glial cells with different electrode materials such as TiN and nanocolumnar TiN surfaces in contrast to gold and ITO substrates. Employing single-cell force spectroscopy, we quantified short-term interaction forces between neuron-like cells (SH-SY5Y cells) and glial cells (U-87 MG cells) for the different materials and contact times. Additionally, results were compared to the spreading dynamics of cells for different culture times as a function of the underlying substrate. The adhesion behavior of glial cells was almost independent of the biomaterial and the maximum growth areas were already seen after one day; however, adhesion dynamics of neurons relied on culture material and time. Neurons spread much better on TiN and nanocolumnar TiN and also formed more neurites after three days in culture. Our designed nanocolumnar TiN offers the possibility for building miniaturized microelectrode arrays for impedance spectroscopy without losing detection sensitivity due to a lowered self-impedance of the electrode. Hence, our results show that this biomaterial promotes adhesion and spreading of neurons and glial cells, which are important for many biomedical applications in vitro and in vivo.

ELECTROCHEMICAL Co3O4 NANOPOROUS THIN FILMS SENSOR FOR HYDROGEN PEROXIDE DETECTION

NANO ◽  
2014 ◽  
Vol 09 (04) ◽  
pp. 1450047 ◽  
Author(s):  
GUANG SHENG CAO ◽  
RUILIN WANG ◽  
PEILONG WANG ◽  
XIN LI ◽  
YUE WANG ◽  
...  
Keyword(s):  
Thin Films ◽  
Hydrogen Peroxide ◽  
Tin Oxide ◽  
Indium Tin Oxide ◽  
Photoelectron Spectroscopy ◽  
Electrode Materials ◽  
Electrochemical Sensing ◽  
Detection Sensitivity ◽  
X Ray ◽  
Hydrogen Peroxide Detection

The nanoporous Co 3 O 4 thin films were prepared on indium tin oxide (ITO) glasses by an electrodeposition method. The surface morphology and composition of the nanoporous Co 3 O 4 films were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDS) and X-ray photoelectron spectroscopy (XPS). The results show that the as-deposited nanoporous Co 3 O 4 film is constructed by many interconnected nanoflakes with thickness of about 40 nm. The cyclic voltammetry (CV) measurement indicates that the nanoporous Co 3 O 4 films exhibit remarkable electrocatalytic activities for the hydrogen peroxide ( H 2 O 2) reduction which shows that it is a good candidate to be employed as electrode materials for electrochemical sensing of H 2 O 2. Further analysis indicated that the detection sensitivity of the sensor was 1.357 mA mM-1 cm-2 and the detection limit was estimated to be about 0.2 mM.

scholarly journals Transparent Microelectrode Arrays Fabricated by Ion Beam Assisted Deposition for Neuronal Cell In Vitro Recordings

Micromachines ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 497 ◽  
Author(s):  
Tomi Ryynänen ◽  
Ropafadzo Mzezewa ◽  
Ella Meriläinen ◽  
Tanja Hyvärinen ◽  
Jukka Lekkala ◽  
...  
Keyword(s):  
Thin Films ◽  
Indium Tin Oxide ◽  
Microelectrode Array ◽  
Electrode Materials ◽  
Ion Beam ◽  
Neuronal Cell ◽  
Measurement Properties ◽  
Ito Thin Films ◽  
Sputter Deposited

Microelectrode array (MEA) is a tool used for recording bioelectric signals from electrically active cells in vitro. In this paper, ion beam assisted electron beam deposition (IBAD) has been used for depositing indium tin oxide (ITO) and titanium nitride (TiN) thin films which are applied as transparent track and electrode materials in MEAs. In the first version, both tracks and electrodes were made of ITO to guarantee full transparency and thus optimal imaging capability. In the second version, very thin (20 nm) ITO electrodes were coated with a thin (40 nm) TiN layer to decrease the impedance of Ø30 µm electrodes to one third (1200 kΩ → 320 kΩ) while maintaining (partial) transparency. The third version was also composed of transparent ITO tracks, but the measurement properties were optimized by using thick (200 nm) opaque TiN electrodes. In addition to the impedance, the optical transmission and electric noise levels of all three versions were characterized and the functionality of the MEAs was successfully demonstrated using human pluripotent stem cell-derived neuronal cells. To understand more thoroughly the factors contributing to the impedance, MEAs with higher IBAD ITO thickness as well as commercial sputter-deposited and highly conductive ITO were fabricated for comparison. Even if the sheet-resistance of our IBAD ITO thin films is very high compared to the sputtered one, the impedances of the MEAs of each ITO grade were found to be practically equal (e.g., 300–370 kΩ for Ø30 µm electrodes with 40 nm TiN coating). This implies that the increased resistance of the tracks, either caused by lower thickness or lower conductivity, has hardly any contribution to the impedance of the MEA electrodes. The impedance is almost completely defined by the double-layer interface between the electrode top layer and the medium including cells.

RAPID IN VITRO PROPAGATION OF WESTERN GHATS CULTIVAR - COLOCASIA ESCULENTA FOR THE HIGH FREQUENCY OF PLANTLETS

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Jyothi R ◽  
Srinivasa Murthy K M ◽  
Hossein . ◽  
Veena .
Keyword(s):  
Tissue Culture ◽  
Wide Distribution ◽  
Colocasia Esculenta ◽  
Limiting Factor ◽  
Promising Alternative ◽  
Rapid Multiplication ◽  
Medical Plant ◽  
Total Fat ◽  
Planting Materials

Colocasia esculenta is commonly known as Taro, it is referred to as cocoyam in Nigeria. They are cherished for their rich taste, nutritional and medicinal properties. Every 100 g of taro corms possess 112 Kcal, 26.46 g carbohydrate, 1.50 g protein, 0.20 g total fat and 4.1g fiber (USDA National Nutrient Data Base). Besides its nutritional value, taro is used as a medical plant and provides bioactive compounds used as an anti-cancer drugs. Traditionally, cocoyams are vegetative propagated from tuber fragments, a practice that encourages pathogen distribution. Colocasia esculenta is a widely distributed food crop in the humid tropics and subtropics. Despite of its wide distribution, Taro plants are commonly infected with DsMV and other pathogens. This virus induces conspicuous mosaic, malformation, dwarfing or feathering on leaves in taro. As the results of infection, it reduces the quality and yield of taro production greatly. This virus is thus considered as a major limiting factor in the production of taro. Here plays the importance of  tissue culture plays a major role in producing the disease resistant plants round the year with high quality. For rapid multiplication and production of quality planting materials, tissue culture technology offers promising alternative compared to the traditional production methods. KEYWORDS: Colocasia esculenta, Virus, Pathogens, Conventional propagation, Micropropagation, Yield, Rapid multiplication, Quality

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

Neural Stem Cells to Glial Cells an Important Factor for Brain Injury

2020 ◽  
Author(s):  
Prithiv K R Kumar
Keyword(s):  
Stem Cells ◽  
Central Nervous System ◽  
Nervous System ◽  
Neural Stem Cells ◽  
Glial Cells ◽  
Brain Injuries ◽  
The Body ◽  
The Central Nervous System ◽  
Near Future

Stem cells have the capacity to differentiate into any type of cell or organ. Stems cell originate from any part of the body, including the brain. Brain cells or rather neural stem cells have the capacitive advantage of differentiating into the central nervous system leading to the formation of neurons and glial cells. Neural stem cells should have a source by editing DNA, or by mixings chemical enzymes of iPSCs. By this method, a limitless number of neuron stem cells can be obtained. Increase in supply of NSCs help in repairing glial cells which in-turn heal the central nervous system. Generally, brain injuries cause motor and sensory deficits leading to stroke. With all trials from novel therapeutic methods to enhanced rehabilitation time, the economy and quality of life is suppressed. Only PSCs have proven effective for grafting cells into NSCs. Neurons derived from stem cells is the only challenge that limits in-vitro usage in the near future.

Development and Ex-vivo Evaluation of Topiramate Mucoadhesive Nanoparticles for Intranasal Delivery

2020 ◽  
Vol 13 (6) ◽  
pp. 5250-5255
Author(s):  
Ashwin Kumar Tulasi ◽  
Anil Goud Kandhula ◽  
Ravi Krishna Velupula
Keyword(s):  
Particle Size ◽  
Nasal Mucosa ◽  
Intranasal Administration ◽  
Ex Vivo ◽  
Chemical Properties ◽  
Brain Targeting ◽  
Oral Tablet ◽  
Promising Alternative ◽  
Ex Vivo Permeation

Topiramate is a second-generation antiepileptic drug used in partial, generalized seizures as an oral tablet. Oral route of administration is most convenient but shows delayed absorption. Moreover, in emergency cases, parenteral administration is not possible as it requires medical assistance. Hence, the present study was aimed to develop topiramate mucoadhesive nanoparticles for intranasal administration using ionotropic gelation method. The developed nanoparticles were evaluated for physico-chemical properties like particle size, zeta potential, surface morphology, drug content, entrapment efficiency, in vitro drug release, mucoadhesive strength, and ex vivo permeation studies in excised porcine nasal mucosa. Optimized nanoparticle formulation (T9) was composed oil mucoadhesive agent (Chitosan 1% w/w), cross linking polymer (TPP) and topiramate 275mg, 100mg and 4% respectively. It showed particle size of 350nm, high encapsulation efficacy and strong mucoadhesive strength. In vitro drug diffusion of optimized formulation showed 95.12% release of drug after 180min. Ex-vivo permeation of drug across nasal mucosa was   88.05 % after 180min. Nasocilial toxicity studies showed optimized formulation did not damage the nasal mucosa. Thus, the intranasal administration of topiramate using chitosan can be a promising alternative for brain targeting and the treatment of epilepsy.

Development and Optimization of Sustained Release Moxifloxacin Hydrochloride Loaded Nanoemulsion for Ophthalmic Drug Delivery: A 32 Factorial Design Approach

2020 ◽  
Vol 12 ◽  
Author(s):  
Sagar R. Pardeshi ◽  
Harshal A. Mistari ◽  
Rakhi S. Jain ◽  
Pankaj R. Pardeshi ◽  
Rahul L. Rajput ◽  
...  
Keyword(s):  
Drug Release ◽  
Sustained Release ◽  
Factorial Design ◽  
Water Solubility ◽  
Tween 80 ◽  
Drug Release Profile ◽  
Sustained Drug Release ◽  
Bacterial Conjunctivitis ◽  
Promising Alternative

Background: Moxifloxacin is a BCS class I drug used in the treatment of bacterial conjunctivitis and keratitis. Despite its high water solubility, it possesses limited bioavailability due to anatomical and physiological constraints associated with the eyes which required multiple administrations to achieve a therapeutic effect. Objective: In order to prolong drug release and to improve antibacterial efficacy for the treatment of bacterial keratitis and conjunctivitis, moxifloxacin loaded nanoemulsion was developed. Methods: The concentration of oil (oleic acid), surfactant (tween 80), and cosurfactant (propylene glycol) were optimized by employing a 3-level 2-factorial design of experiment for the development of nanoemulsion. The developed nanoemulsion was characterized by particle size distribution, viscosity, refractive index, pH, drug content and release, transmission electron microscopy (TEM), and antibacterial study. The compatibility of the drug with the excipients was accessed by Fourier transform infrared spectroscopy (FTIR). Result: The average globule size was found to be 198.20 nm. The TEM study reveals the globules were nearly spherical and are well distributed. In vitro drug release profile for nanoemulsion shown sustained drug release (60.12% at the end of 6 h) compared to drug solution, where complete drug released within 2 h. The antibacterial effectiveness of the drug-loaded nanoemulsion was improved against S. aureus compared with the marketed formulation. Conclusion: The formulated sustained release nanoemulsion could be a promising alternative to eye drop with improved patient compliance by minimizing dosing frequency with improved antibacterial activity.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

2020 ◽  
Vol 13 (5) ◽  
pp. 641-649
Author(s):  
Yuancheng Li ◽  
Yaqi Cui ◽  
Xiaolong Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Clustering Algorithm ◽  
Characteristic Curve ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Residential Areas ◽  
Processing Power ◽  
Learning Machine ◽  
Advanced Metering ◽  
Matlab Programming

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

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