Search results for "Electronic"
showing 10 items of 17076 documents
Nanoparticle delivery to metastatic breast cancer cells by nanoengineered mesenchymal stem cells
2017
We created a 3D cell co-culture model by combining nanoengineered mesenchymal stem cells (MSCs) with the metastatic breast cancer cell line MDA-MD-231 and primary breast cancer cell line MCF7 to explore the transfer of quantum dots (QDs) to cancer cells. First, the optimal conditions for high-content QD loading in MSCs were established. Then, QD uptake in breast cancer cells was assessed after 24 h in a 3D co-culture with nanoengineered MSCs. We found that incubation of MSCs with QDs in a serum-free medium provided the best accumulation results. It was found that 24 h post-labelling QDs were eliminated from MSCs. Our results demonstrate that breast cancer cells efficiently uptake QDs that a…
Local field potential activity dynamics in response to deep brain stimulation of the subthalamic nucleus in Parkinson's disease
2020
Abstract Local field potentials (LFPs) may afford insight into the mechanisms of action of deep brain stimulation (DBS) and potential feedback signals for adaptive DBS. In Parkinson's disease (PD) DBS of the subthalamic nucleus (STN) suppresses spontaneous activity in the beta band and drives evoked resonant neural activity (ERNA). Here, we investigate how STN LFP activities change over time following the onset and offset of DBS. To this end we recorded LFPs from the STN in 14 PD patients during long (mean: 181.2 s) and short (14.2 s) blocks of continuous stimulation at 130 Hz. LFP activities were evaluated in the temporal and spectral domains. During long stimulation blocks, the frequency …
A new approach for the treatment of CLL using chlorambucil/hydroxychloroquine-loaded anti-CD20 nanoparticles
2015
Current approaches for the treatment of chronic lymphocytic leukemia (CLL) have greatly improved the prognosis for survival, but some patients remain refractive to these therapeutic regimens. Hence, in addition to reducing the long-term sideeffects of therapeutics for all leukemia patients, there is an urgent need for novel therapeutic strategies for difficult-to-treat leukemia cases. Due to the cytotoxicity of drugs, the major challenge currently is to deliver the therapeutic agents to neoplastic cells while preserving the viability of non-malignant cells. In this study, we propose a therapeutic approach in which high doses of hydroxychloroquine and chlorambucil were loaded into biodegrada…
Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters
2017
Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Se…
The Drosophila Larval Locomotor Circuit Provides a Model to Understand Neural Circuit Development and Function
2021
It is difficult to answer important questions in neuroscience, such as: “how do neural circuits generate behaviour?,” because research is limited by the complexity and inaccessibility of the mammalian nervous system. Invertebrate model organisms offer simpler networks that are easier to manipulate. As a result, much of what we know about the development of neural circuits is derived from work in crustaceans, nematode worms and arguably most of all, the fruit fly, Drosophila melanogaster. This review aims to demonstrate the utility of the Drosophila larval locomotor network as a model circuit, to those who do not usually use the fly in their work. This utility is explored first by discussion…
Spectral entropy based neuronal network synchronization analysis based on microelectrode array measurements
2016
Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from differ…
A new parallel pipeline for DNA methylation analysis of long reads datasets
2017
Background DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years. However, the current trend is that the new sequencers and the ones expected for an upcoming future yield sequences of increasing length, making these software tools inefficient and obsolete. Results In this paper, we propose a new software based on a strategy for methylation analysis of Methyl-seq sequencing data that requires much shorter execution times while…
Revealing community structures by ensemble clustering using group diffusion
2018
We propose an ensemble clustering approach using group diffusion to reveal community structures in data. We represent data points as a directed graph and assume each data point belong to single cluster membership instead of multiple memberships. The method is based on the concept of ensemble group diffusion with a parameter to represent diffusion depth in clustering. The ability to modulate the diffusion-depth parameter by varying it within a certain interval allows for more accurate construction of clusters. Depending on the value of the diffusion-depth parameter, the presented approach can determine very well both local clusters and global structure of data. At the same time, the ability …
Automatic detection of hemangiomas using unsupervised segmentation of regions of interest
2016
In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…
Deep learning network for exploiting positional information in nucleosome related sequences
2017
A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…