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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
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Types of RNA01:20

Types of RNA

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...
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  1. Home
  3. Research Domains
  5. Health Sciences
  7. Epidemiology
  9. Major Global Burdens Of Disease
  11. Long Non-coding Rnas And Alzheimer's Disease: Towards Personalized Diagnosis.
  1. Home
  3. Research Domains
  5. Health Sciences
  7. Epidemiology
  9. Major Global Burdens Of Disease
  11. Long Non-coding Rnas And Alzheimer's Disease: Towards Personalized Diagnosis.

Related Experiment Video

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease
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Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

Published on: May 20, 2024

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Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.

Maria I Mosquera-Heredia1, Oscar M Vidal1, Luis C Morales1

  • 1Department of Medicine, Universidad del Norte, Barranquilla 081007, Colombia.

International Journal of Molecular Sciences
|July 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers identified two long non-coding RNAs (lncRNAs) as promising biomarkers for diagnosing Alzheimer's disease (AD). Machine learning models achieved over 95% accuracy, suggesting potential for personalized AD diagnostics in underserved communities.

Keywords:
Alzheimer’s diseaseexosomeslong non-coding RNAmachine learning

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Area of Science:

  • Neuroscience
  • Genetics
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia.
  • Current AD diagnosis relies on a combination of clinical assessments and imaging, lacking a definitive single test, especially in diverse populations.
  • Exosomes and their RNA cargo, particularly long non-coding RNAs (lncRNAs), are implicated in AD pathogenesis but their diagnostic potential is underexplored.

Purpose of the Study:

  • To investigate the potential of lncRNAs as diagnostic markers for Alzheimer's disease.
  • To identify differentially expressed lncRNAs in individuals with AD compared to controls.
  • To develop and validate machine learning models for AD diagnosis based on lncRNA expression profiles.

Main Methods:

personalized medicine
  • Clinical, cognitive, and genetic characterization of 15 AD patients and 15 controls from Barranquilla, Colombia.
  • Quantification of 28,909 lncRNAs using advanced bioinformatics and analytics.
  • Application of Machine Learning (ML) models to identify diagnostic lncRNA signatures.

Main Results:

  • Eighteen differentially expressed lncRNAs were identified, with 18 linked to pivotal AD-related genes.
  • Two specific lncRNAs, ENST00000608936 and ENST00000433747, emerged as strong diagnostic candidates.
  • ML models demonstrated >95% sensitivity, specificity, and accuracy for AD diagnosis in both training and testing datasets.

Conclusions:

  • lncRNA expression profiles hold significant promise for advancing personalized Alzheimer's disease diagnosis.
  • The identified lncRNAs offer potential for early detection and follow-up strategies, particularly in understudied communities.
  • This study highlights a novel, data-driven approach to biomarker discovery for AD.