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

The Fluid Mosaic Model01:34

The Fluid Mosaic Model

147.0K
The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.
147.0K
Fluid Mosaic Model01:19

Fluid Mosaic Model

11.6K
Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich...
11.6K

You might also read

Related Articles

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

Sort by
Same author

CAR Practice Guidelines on Breast Imaging and Interventions: Mammography and Digital Breast Tomosynthesis.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

CAR Practice Guidelines on Breast Imaging and Interventions: Breast Magnetic Resonance Imaging.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

CAR Practice Guidelines on Breast Imaging and Interventions: Contrast-Enhanced Mammography.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

CAR Practice Guidelines on Breast Imaging and Interventions: Breast Ultrasound.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

CAR Practice Guidelines on Breast Imaging and Interventions: Breast Intervention and Biopsy Procedures.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes·2026
Same author

The Impact of Artificial Intelligence on Radiology Specialty Preferences Among Canadian Medical Students and Residents.

Academic radiology·2026
Same journal

Changes in C-reactive protein levels over time in high-temperature environments using postmortem blood.

Forensic science international·2026
Same journal

Insights from the first synthetic cannabinoid clandestine lab dismantled in Brazil.

Forensic science international·2026
Same journal

Determination of the new psychoactive substances MDMB-4en-PINACA, ADB-BUTINACA and some of their metabolites in blood and urine using DLLE-LC-MS/MS: application to real forensic case samples.

Forensic science international·2026
Same journal

The revolver halo as a forensic marker: Raman spectroscopic evidence of primer-driven gunshot residue deposition.

Forensic science international·2026
Same journal

Research on the effects of signature size on experts' opinions.

Forensic science international·2026
Same journal

Experimental and numerical study of non-penetrating FMJ ballistic impacts on coupled soft and bone tissue surrogates.

Forensic science international·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2025

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

15.7K

Developing an interpretation model for body fluid identification.

Courtney R H Lynch1, Rachel Fleming2, James M Curran3

  • 1Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, New Zealand; School of Chemistry, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

Forensic Science International
|April 30, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models, including multinomial logistic regression, accurately identify body fluid types from messenger RNA (mRNA) profiles using real-time quantitative reverse-transcription PCR (RT-qPCR). These advanced methods improve forensic analysis, particularly for challenging samples like menstrual fluid.

Keywords:
Body fluididentificationMRNA profilingProbabilistic interpretation methodQuantitative PCR

More Related Videos

Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles
09:07

Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles

Published on: May 24, 2024

877
Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.0K

Related Experiment Videos

Last Updated: Jun 27, 2025

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

15.7K
Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles
09:07

Author Spotlight: Accelerating Diagnostic Accuracy with Direct Identification of Gram-Negatives from Blood Culture Bottles

Published on: May 24, 2024

877
Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.0K

Area of Science:

  • Forensic science
  • Molecular biology
  • Machine learning

Background:

  • Criminal investigations, especially sexual assaults, require body fluid identification for context.
  • Conventional methods struggle to identify certain fluids like menstrual and vaginal materials.
  • Endpoint reverse-transcription PCR (RT-PCR) is used for body fluid-specific messenger RNA (mRNA) detection.

Purpose of the Study:

  • To apply and assess machine learning models for body fluid identification using single-source mRNA profiles from real-time quantitative reverse-transcription PCR (RT-qPCR).
  • To compare the performance of multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) in classifying body fluids.
  • To evaluate the utility of quantitative threshold cycle (Cq) values versus presence/absence data in classification.

Main Methods:

  • RT-qPCR was used to obtain mRNA profiles from saliva, blood, menstrual fluid, vaginal material, and semen.
  • Machine learning models including MLR, NB, and LDA were applied to classify the body fluid types.
  • Receiver operating characteristic (ROC) curves were used to analyze classification performance per body fluid type.

Main Results:

  • Multinomial logistic regression (MLR) demonstrated the highest performance, achieving an approximate overall accuracy of 0.95 with quantitative Cq values.
  • Quantitative Cq data from RT-qPCR provided improved classification accuracy compared to presence/absence data.
  • All tested methods exhibited challenges in accurately classifying menstrual blood samples.

Conclusions:

  • Machine learning models, particularly MLR, are effective for classifying body fluids from RT-qPCR mRNA profiles.
  • Quantitative RT-qPCR data significantly aids in accurate body fluid identification.
  • Further research is needed to address classification difficulties with menstrual fluid and to model body fluid mixtures.