Brb Array Tools
Brb Array Tools - Related Videos
16:26
Setting-up an In Vitro Model of Rat Blood-brain Barrier (BBB): A Focus on BBB Impermeability and Receptor-mediated Transport
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Cited by 55 •
2014
The aim of the present study was to validate the reproducibility of an in vitro BBB model involving a rat syngeneic co-culture of endothelial cells and astrocytes. The endothelial cell monolayer presented high TEER and low LY permeability. Expression of specific TJ proteins, functional responses to inflammation and functionality of transporters and receptors were assessed.
02:53
Blood-Brain Barrier Mimic Permeability Assay: An In Vitro Assay to Assess the Permeability of BBB Mimic to Tracer Molecules
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2023
This video describes a permeability assay to evaluate the permeability of in vitro blood-brain barrier using sodium fluorescein molecules.
09:16
Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Cited by 7 •
2015
Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a diagnostic service laboratory.
07:02
Blood-Brain Tumor Barrier or BBTB Model: An Adaption of the Blood-Brain Barrier or BBB Model to Evaluate Delivery of Therapeutics to the Tumor
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2023
In this video, we describe a comprehensive protocol to establish an in vitro model mimicking the blood-brain tumor barrier. The model holds promise to understand the delivery of therapeutic agents for the treatment of brain tumors.
10:32
Brain Mapping Using a Graphene Electrode Array
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2023
We present a graphene array-based brain mapping procedure to reduce the invasiveness and improve spatiotemporal resolution. Graphene array-based surface electrodes exhibit long-term biocompatibility, mechanical flexibility, and suitability for brain mapping in a convoluted brain. This protocol allows for constructing multiple forms of sensory maps simultaneously and sequentially.
FAQs
These stages prepare probe-level measurements for downstream interpretation. Quality assessment examines the reliability of the input data, normalization places measurements into a form suitable for comparison, and filtering focuses analysis on selected expression measurements. Together, they influence which genes or patterns enter statistical testing, so choices made before analysis can affect the biological conclusions drawn from the dataset.
Each method addresses a different analytical question. Class comparison identifies genes associated with predefined experimental groups, whereas class prediction evaluates patterns that distinguish those groups. Clustering searches for structure or subgroups without relying solely on predefined categories, and survival analysis examines relationships between expression patterns and clinical outcomes. Selecting the method depends on the study design and biological question.
Microarray experiments can produce large collections of individual probe measurements, but biological interpretation usually requires identifying genes, expression patterns, or signatures associated with a condition. BRB-ArrayTools supports this transition by combining data preparation with statistical analysis. The resulting patterns can be related to disease classification, treatment response, molecular subtypes, or gene-function relationships rather than remaining as uninterpreted measurement lists.
A typical workflow begins with quality assessment, followed by normalization and filtering of the expression data. Researchers then select an analysis suited to their question, such as class comparison, class prediction, clustering, or survival analysis. The software helps identify genes or patterns associated with groups or clinical outcomes, after which investigators can interpret the findings biologically and develop hypotheses for laboratory study.
Researchers can apply the suite when they need to examine gene-expression patterns across experimental groups or connect molecular measurements with clinical outcomes. Supported uses include studying disease classification, treatment response, molecular subtypes, and gene-function relationships. These applications allow investigators to evaluate genomic signatures and identify patterns that may help explain biological differences among samples or patient groups.
The analysis can identify genes, expression patterns, or genomic signatures associated with experimental categories or clinical outcomes. Such results may support evaluation of molecular subtypes, treatment-related responses, or relationships between expression and gene function. Because these findings generate hypotheses for further laboratory investigation, they provide a starting point for follow-up biological studies rather than replacing experimental validation.