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Development of a hybrid fluid-structure interaction (FSI) algorithm to model red blood cell deformation and oxygen-dependent ATP release under flow stresses

2025· article· en· W4411847243 on OpenAlexaff
Keith C. Afas, Daniel Goldman

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsWestern University
Fundersnot available
KeywordsDeformation (meteorology)OxygenFluid–structure interactionFlow (mathematics)Red CellBiophysicsMechanicsAlgorithmRed blood cellBiological systemChemistryMaterials scienceComputer sciencePhysicsThermodynamicsBiologyComposite materialArtificial intelligenceBiochemistryFinite element method

Abstract

fetched live from OpenAlex

The microcirculation serves to deliver oxygen (O 2 ) to tissue as red blood cells (RBCs) pass through the smallest blood vessels in the body: capillaries. Imaging techniques quantify O 2 present in capillaries but lack effective modalities quantifying O 2 entering tissue from capillaries. Thus, mathematical simulation has been used to investigate how O 2 is distributed locally over a variation of metabolic tissue O 2 demands. It has also been used to investigate mechanisms regulating capillary blood flow to meet such demands. Distributed throughout the microcirculation, RBCs have been hypothesized as potential candidates initiating signals at the capillary level that are transmitted upstream to arterioles, thereby altering capillary blood flow. It has been found that RBC deformation, as well as oxyhemoglobin desaturation, can cause release of adenosine triphosphate (ATP). It has been theorized that as RBCs deform with local blood flow, released ATP modulates upstream vessel diameter, but requires mathematical modelling to systematically investigate. A condensed matter model of RBC deformation was developed in the past, with the ability to predict baseline red blood cell shapes (at the micron level), and with the goal of reliably quantifying forces of the RBC on the surrounding environment, owing to its unique elasticity. To investigate how this RBC elasticity interacts with blood flow, the objective of this study was the development of a comprehensive hybrid fluid-structure interaction algorithm. This was done using a novel Discrete Exterior Calculus (DEC) solver for fluid flow, allowing the model to be geometrically versatile. This algorithm was developed to be capable of theoretically simulating RBC deformation in response to a range of shear stress rates in units of [1/ms]. The algorithm allowed detailed tracking of RBC shape changes in response to shear forces (%), osmolarity variations, and membrane tension, making it a powerful tool for understanding the mechanical behavior of RBCs in vivo. Variations in blood flow, and the effect on RBC geometry and shear, are quantified and presented. Using DEC's geometric versatility, fluid simulations through a variety of microvascular geometries will be presented, and deformation parameters will be outlined. Future work includes validation of the algorithm against experimental in vivo data, confirming the accuracy of the model in replicating observed RBC behaviour under varying flow conditions. Furthermore, interpreting the shear response of the RBC to flow in the context of ATP release will be investigated. By quantifying RBC deformation under fluid stresses, and estimating ATP release parameters, a novel approach to understanding how RBCs interact with their microcirculatory environment will be obtained. These findings provide a quantitative framework for understanding how RBCs contribute to microcirculatory regulation and offer a new tool for studying pathophysiological conditions where blood flow and oxygen delivery are compromised. Acknowledgements would like to be given to various NSERC Grants (#R4081A03, CGS-M, CGS-D) This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.245
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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