Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Efficacy and tolerability of metformin as an adjuvant therapy in patients with relapse-remitting multiple sclerosis receiving interferon Beta 1a: A randomized pilot trial.

Journal of neuroimmunology·2026

Same author

Efficacy of ultrasound-guided internal jugular vein versus subclavian vein cannulation in neonates and infants weighing less than 5 kg: a randomized controlled trial.

Anaesthesia, critical care & pain medicine·2025

Same author

Characterization of the Dynamic Flow Response in Microfluidic Devices.

Small methods·2024

Same author

Recent Breakthroughs in Using Quantum Dots for Cancer Imaging and Drug Delivery Purposes.

Nanomaterials (Basel, Switzerland)·2023

Same author

Regression Analysis between the Different Breast Dose Quantities Reported in Digital Mammography and Patient Age, Breast Thickness, and Acquisition Parameters.

Journal of imaging·2022

Same author

Phenotyping of the thrashing forces exerted by partially immobilized C. elegans using elastomeric micropillar arrays.

Lab on a chip·2019

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Biophysical Characterization of Flagellar Motor Functions

Biophysical Characterization of Flagellar Motor Functions

Published on: January 18, 2017

Analysis of sperm beating characteristics using microfluidic trapping and machine-learning-based flagellum tracking.

Aisha Hamidu¹, Omar Abdelgawad², Ahmed Azmeer³

¹Biomedical Engineering Program, American University of Sharjah, Sharjah, United Arab Emirates.

|October 2, 2025

Summary

This study introduces a microfluidic platform for detailed sperm flagellar analysis, aiding in diagnosing male infertility. The system uses machine learning to precisely measure sperm beating patterns, offering new insights into reproductive health.

More Related Videos

Fish Sperm Assessment Using Software and Cooling Devices

Fish Sperm Assessment Using Software and Cooling Devices

Published on: July 28, 2018

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Published on: June 6, 2019

Related Experiment Videos

Last Updated: Jan 16, 2026

Biophysical Characterization of Flagellar Motor Functions

Biophysical Characterization of Flagellar Motor Functions

Published on: January 18, 2017

Fish Sperm Assessment Using Software and Cooling Devices

Fish Sperm Assessment Using Software and Cooling Devices

Published on: July 28, 2018

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract

Published on: June 6, 2019

Area of Science:

Biomedical Engineering
Reproductive Biology
Microfluidics

Background:

Male infertility affects many couples, with standard semen analysis often insufficient for diagnosing functional sperm deficits.
Unexplained infertility necessitates advanced methods to assess sperm performance beyond basic motility parameters.

Purpose of the Study:

To develop and validate a novel microfluidic platform for high-resolution characterization of sperm flagellar beating patterns.
To investigate the impact of chemical stimuli and fluid flow on sperm motility and flagellar dynamics.

Main Methods:

A microfluidic device was engineered using microcontact printing and fibronectin spots to immobilize sperm heads.
A YOLOv8 (You Only Look Once) machine learning model was trained for precise flagellar keypoint tracking and analysis.
Sperm flagellar beating parameters (amplitude, frequency, asymmetry) were quantified under various conditions, including chemical treatments and flow.

Main Results:

Caffeine and heparin treatments significantly altered flagellar beating amplitude and frequency, mimicking capacitation and hyperactivation.
Sperm flagellar beating frequency decreased under simulated physiological flow conditions.
The platform demonstrated high-throughput analysis capabilities, correlating flagellar patterns with functional sperm parameters.

Conclusions:

The microfluidic platform provides detailed insights into sperm functional parameters linked to male infertility.
This technology offers a valuable tool for investigating idiopathic male infertility and evaluating therapeutic strategies.
The system overcomes limitations of traditional sperm analysis, enabling precise characterization under controlled conditions.