Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.0K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.0K
Detection of Black Holes01:10

Detection of Black Holes

2.6K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.4K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

666
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
666
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

11.4K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.4K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

6.0K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
6.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A prospective randomized comparison of early embryo cleavage kinetics between two media culture systems.

Pakistan journal of medical sciences·2017
Same author

Transcript profile analysis reveals important roles of jasmonic acid signalling pathway in the response of sweet potato to salt stress.

Scientific reports·2017
Same author

Pnserpin: A Novel Serine Protease Inhibitor from Extremophile Pyrobaculum neutrophilum.

International journal of molecular sciences·2017
Same author

The OsHAPL1-DTH8-Hd1 complex functions as the transcription regulator to repress heading date in rice.

Journal of experimental botany·2017
Same author

Spliced leader-based analyses reveal the effects of polycyclic aromatic hydrocarbons on gene expression in the copepod Pseudodiaptomus poplesia.

Aquatic toxicology (Amsterdam, Netherlands)·2017
Same author

DNA barcoding species in Alexandrium tamarense complex using ITS and proposing designation of five species.

Harmful algae·2017
Same journal

Long-term molecular embedding of early life adversity: leukocyte DNA methylation and gene expression associations from childhood to young adulthood.

Epigenomics·2026
Same journal

Overview of transcriptomics and epigenomics approaches in the diagnosis, prognosis, and therapeutics of primary brain cancers.

Epigenomics·2026
Same journal

Ultra-sensitive urine DNA methylation test enables early and accurate detection of bladder cancer.

Epigenomics·2026
Same journal

Promoter methylation-associated brain-enriched long noncoding RNAs in glioblastoma: a multi-cohort public epigenomic re-analysis.

Epigenomics·2026
Same journal

Clinical diagnostic utility of <i>MGMT</i> promoter methylation.

Epigenomics·2026
Same journal

DNA methylation in glioblastoma: insights from single-cell epigenomics.

Epigenomics·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Detecting Cortex Fragments During Bacterial Spore Germination
08:35

Detecting Cortex Fragments During Bacterial Spore Germination

Published on: June 25, 2016

9.9K

Detection of m

Xing-Bo Mo1,2,3, Shu-Feng Lei1,2,3, Yong-Hong Zhang1,3

  • 1Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, Soochow University, 199 Renai Road, Suzhou, Jiangsu 215123, PR China.

Epigenomics
|September 18, 2018
PubMed
Summary
This summary is machine-generated.

This study identified numerous m6A-single nucleotide polymorphisms (SNPs) linked to coronary artery disease (CAD). One key SNP, rs12286, influences m6A methylation and ADAMTS7 gene expression, offering insights into CAD development.

Keywords:
ADAMTS7FTOcoronary artery diseaseeQTLgene expressiongenome-wide association studym6Amultifunctional variantssingle nucleotide polymorphismtranscription regulation

More Related Videos

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
07:21

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells

Published on: September 17, 2021

4.0K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.5K

Related Experiment Videos

Last Updated: Feb 5, 2026

Detecting Cortex Fragments During Bacterial Spore Germination
08:35

Detecting Cortex Fragments During Bacterial Spore Germination

Published on: June 25, 2016

9.9K
Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
07:21

Automated, High-Throughput Detection of Bacterial Adherence to Host Cells

Published on: September 17, 2021

4.0K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.5K

Area of Science:

  • Genetics
  • Molecular Biology
  • Cardiovascular Research

Background:

  • Coronary artery disease (CAD) is a leading cause of mortality worldwide.
  • The role of RNA methylation, specifically N6-methyladenosine (m6A), in CAD pathogenesis is an emerging area of research.
  • Single nucleotide polymorphisms (SNPs) can influence gene expression and disease susceptibility.

Purpose of the Study:

  • To investigate the association between m6A-SNPs and the risk of developing coronary artery disease (CAD).
  • To explore the functional implications of identified m6A-SNPs in the context of CAD.
  • To identify specific genetic variants that may contribute to CAD through alterations in m6A methylation patterns.

Main Methods:

  • Examined the association of m6A-SNPs with CAD in a large cohort of approximately 185,000 cases and controls.
  • Performed expression quantitative trait loci (eQTL) analyses to link genetic variants to gene expression.
  • Conducted differential expression analyses to identify genes with altered expression levels in CAD.

Main Results:

  • Identified 304 m6A-SNPs significantly associated with CAD (p < 0.05) out of 4390 detected m6A-SNPs.
  • SNP rs12286 showed a genome-wide significant association with CAD (p = 4.5 × 10-9).
  • rs12286 was predicted to affect m6A methylation and alter regulatory motif binding, potentially impacting ADAMTS7 gene expression (p = 1.26 × 10-8).

Conclusions:

  • The study identified a substantial number of m6A-SNPs associated with coronary artery disease.
  • Demonstrated the potential functional relevance of these identified SNPs in the context of CAD.
  • Highlights the role of m6A methylation modifications influenced by genetic variations in CAD etiology.