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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

924
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
924
Limiting Reactant02:27

Limiting Reactant

70.5K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
70.5K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.1K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.1K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Random Error01:04

Random Error

9.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.8K
Margin of Error01:27

Margin of Error

7.7K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.7K

You might also read

Related Articles

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

Sort by
Same author

White Matter N-Acylphosphatidylserines (NAPSs) and Myelin Dysfunction in Late-Onset Alzheimer's Disease (LOAD): A Pilot Study.

Life (Basel, Switzerland)·2026
Same author

Copepod Lipidomics: Fatty Acid Substituents of Structural Lipids in <i>Labidocerca aestiva</i>, a Dominant Species in the Food Chain of the Apalachicola Estuary of the Gulf of Mexico.

Life (Basel, Switzerland)·2025
Same author

Metabolic and Lipid Biomarkers for Pathogenic Algae, Fungi, Cyanobacteria, Mycobacteria, Gram-Positive Bacteria, and Gram-Negative Bacteria.

Metabolites·2024
Same author

Comparative Lipidomics of Oral Commensal and Opportunistic Bacteria.

Metabolites·2024
Same author

Pilot Lipidomics Study of Copepods: Investigation of Potential Lipid-Based Biomarkers for the Early Detection and Quantification of the Biological Effects of Climate Change on the Oceanic Food Chain.

Life (Basel, Switzerland)·2023
Same author

Construction of a Bacterial Lipidomics Analytical Platform: Pilot Validation with Bovine Paratuberculosis Serum.

Metabolites·2023

Related Experiment Video

Updated: Feb 12, 2026

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
08:29

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD

Published on: October 10, 2012

16.8K

Lipidomics biomarker studies: Errors, limitations, and the future.

Paul L Wood1, John E Cebak2

  • 1Metabolomics Unit, College of Veterinary Medicine, Lincoln Memorial University, 6965 Cumberland Gap Pkwy, Harrogate, TN 37752, USA.

Biochemical and Biophysical Research Communications
|March 30, 2018
PubMed
Summary
This summary is machine-generated.

High-quality lipidomics data is crucial for clinical translation. Addressing issues like poor data quality, misidentifications, and quantification methods is essential for scientific integrity and biomarker research.

Keywords:
Absolute quantitationHigh-resolution mass spectrometryInternal standardsIsobarsLipidomics

More Related Videos

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.3K
Lipidomics and Transcriptomics in Neurological Diseases
09:58

Lipidomics and Transcriptomics in Neurological Diseases

Published on: March 18, 2022

4.0K

Related Experiment Videos

Last Updated: Feb 12, 2026

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
08:29

Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD

Published on: October 10, 2012

16.8K
On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.3K
Lipidomics and Transcriptomics in Neurological Diseases
09:58

Lipidomics and Transcriptomics in Neurological Diseases

Published on: March 18, 2022

4.0K

Area of Science:

  • Lipidomics, a subfield of metabolomics, involves the comprehensive study of lipids and their peroxidation products.

Background:

  • Many lipidomics publications suffer from poor data quality due to unit mass resolution, leading to lipid misidentifications.
  • Scientific oversight is lacking regarding isobar issues, sample collection, and storage, complicating accurate lipid analysis.
  • Distinguishing between relative and absolute quantification is critical, as obfuscation can mislead readers and erode trust.

Purpose of the Study:

  • To highlight critical issues in lipidomics data quality and scientific practices.
  • To emphasize the need for improved methodologies and oversight in lipidomics research.
  • To underscore the importance of high-quality data for translating biomarker research into clinical practice.

Main Methods:

  • The abstract does not detail specific methods but discusses common challenges in lipidomics data acquisition and interpretation.

Main Results:

  • Poor data quality, lipid misidentifications, and issues with quantification methods are prevalent in lipidomics research.
  • Lack of attention to isobaric interference, sample handling, and storage contributes to unreliable results.

Conclusions:

  • Addressing data quality, methodological rigor, and transparent reporting in lipidomics is imperative.
  • High-quality lipidomics data is fundamental for the successful translation of biomarker discoveries to clinical applications.