Biomarker Identification
Biomarker Identification - Related Videos
13:19
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Cited by 16 •
2013
Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
08:28
An Overview of bGDGT Biomarker Analysis for Paleoclimatology
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types...
04:22
Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles
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Cited by 3 •
2024
This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.
07:24
Sonication Extraction of Lipid Biomarkers from Sediment
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst The material comprising the living "organic" share of any ecosystem (leaves, fungi, bark, tissue; Figure 1) differs fundamentally from the material of the non-living "inorganic" share (rocks and their constituent minerals, oxygen, water, metals). Organic material contains carbon linked to a series of other carbon and hydrogen molecules (Figure 2), which distinguishes it from inorganic material. Carbon's wide valency range...
FAQs
Each data type captures a different aspect of neurological biology. Molecular and genetic measures can reveal disease-related biological changes, physiological measures can reflect altered nervous-system function, and neuroimaging can identify differences in brain-related signals. Comparing these sources helps researchers determine which measurable features best distinguish clinical groups, indicate disease mechanisms, or predict outcomes.
Statistical testing evaluates whether observed differences between individuals or clinical groups are likely to represent meaningful signals rather than chance findings. Independent validation then examines whether those signals remain reliable in a separate assessment. Together, these steps help establish reliability and specificity, reducing the risk that a candidate biomarker reflects only the original study group.
A biomarker panel combines multiple measurable features to support more dependable patient stratification or classification. Individual signals may not capture the full variation associated with a neurological condition, whereas a set of markers can provide complementary information. When independently validated, panels may help distinguish clinical groups more effectively and guide more targeted research.
Researchers can compare molecular, genetic, physiological, or neuroimaging signals across individuals and clinical groups to identify patterns associated with neurological disorders. These patterns may reveal which biological or functional changes accompany disease-related states. Linking measurable signals with disease features can therefore support mechanistic investigation, rather than limiting biomarker work to detection or classification alone.
A typical workflow begins by selecting measurable molecular, genetic, physiological, or neuroimaging features and comparing them across relevant individuals or clinical groups. Researchers then apply statistical testing to evaluate group differences, assess whether candidate signals are reliable and specific, and perform independent validation. Promising results can support classification, monitoring, or prediction of outcomes.
Researchers may use it to support earlier detection and classification of neurological disorders, investigate disease mechanisms, or monitor progression over time. The same approach can assess response to therapy by tracking relevant signals across research or clinical groups. These uses make biomarker studies valuable for both understanding neurological disease and evaluating targeted therapeutic strategies.
Reliable biomarkers can help divide patients into groups according to disease-related features, predicted outcomes, or response to therapy. This patient stratification may allow research studies to focus on more comparable participants and may support more targeted approaches to neurological care. Their usefulness depends on demonstrating reliability and specificity through statistical assessment and independent validation.