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

You might also read

Related Articles

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

Sort by
Same author

Two models of the structures of the lamellae in human meibum and the tear film lipid layer, TFLL.

The ocular surface·2025
Same author

Prevalence Estimation Methods for Time-Dependent Antibody Kinetics of Infected and Vaccinated Individuals: A Markov Chain Approach.

Bulletin of mathematical biology·2025
Same author

Safety and Efficacy of Hydroxypropyl Guar-Hyaluronic Acid Dual-Polymer Lubricating Eye Drops in Indian Subjects with Dry Eye: A Phase IV Study.

Ophthalmology and therapy·2024
Same author

High Resolution Images of Human Meibum Spread on Saline.

Investigative ophthalmology & visual science·2024
Same author

Multi-symptom Relief with Propylene Glycol-Hydroxypropyl-Guar Nanoemulsion Lubricant Eye Drops in Subjects with Dry Eye Disease: A Post-Marketing Prospective Study.

Ophthalmology and therapy·2023
Same author

Modeling in higher dimensions to improve diagnostic testing accuracy: Theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays.

PloS one·2023

Related Experiment Video

Updated: Dec 19, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

9.1K

Parameter Estimation for Evaporation-Driven Tear Film Thinning.

Rayanne A Luke1, Richard J Braun2, Tobin A Driscoll2

  • 1Department of Mathematical Sciences, University of Delaware, Newark, DE, 19716, USA. rayanne@udel.edu.

Bulletin of Mathematical Biology
|June 8, 2020
PubMed
Summary

Researchers estimated tear film parameters using fluid dynamics models and fluorescent intensity data. Optimal values generally align with known ranges, though some suggest broader parameter acceptance in tear film dynamics.

Keywords:
Dry eyeFluorescent imagingOptimizationTear film

More Related Videos

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films
09:32

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

Published on: January 26, 2016

8.5K
Film Control to Study Contributions of Waves to Droplet Impact Dynamics on Thin Flowing Liquid Films
07:08

Film Control to Study Contributions of Waves to Droplet Impact Dynamics on Thin Flowing Liquid Films

Published on: August 18, 2018

7.7K

Related Experiment Videos

Last Updated: Dec 19, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

9.1K
Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films
09:32

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

Published on: January 26, 2016

8.5K
Film Control to Study Contributions of Waves to Droplet Impact Dynamics on Thin Flowing Liquid Films
07:08

Film Control to Study Contributions of Waves to Droplet Impact Dynamics on Thin Flowing Liquid Films

Published on: August 18, 2018

7.7K

Area of Science:

  • Ophthalmology
  • Biophysics
  • Fluid Dynamics

Background:

  • Tear film dynamics are influenced by numerous parameters, with some values not well-established.
  • Understanding these parameters is crucial for diagnosing and managing dry eye disease and other ocular surface conditions.

Purpose of the Study:

  • To estimate key parameters governing tear film thickness and fluorescent intensity distributions over time.
  • To validate thin film fluid dynamics models using experimental fluorescent intensity data from normal subjects.

Main Methods:

  • Nonlinear partial differential equations modeling tear film thickness, osmolarity, and fluorescein concentration were employed.
  • Parameter estimation was performed by fitting model-derived fluorescent intensity to experimentally recorded data using least squares error minimization.
  • Both circular (spot) and linear (streak) tear film breakup models were considered.

Main Results:

  • Parameter estimation results exhibited variability across different subjects and experimental trials.
  • Optimal parameter values derived from the fitting process generally fell within established experimental ranges for tear film dynamics.
  • Certain instances indicated that a wider range of parameter values might be acceptable for accurately describing tear film behavior.

Conclusions:

  • The study successfully estimated parameters influencing tear film dynamics using a combination of computational modeling and experimental data.
  • The findings support the use of fluid dynamics models for analyzing tear film behavior, while also highlighting the inherent variability and potential flexibility in parameter values.
  • Further research may refine parameter ranges and improve diagnostic capabilities for ocular surface diseases.