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Numerical analysis of oxygen uptake processes by red blood cells in stopped-flow measurements: Effects of cell shape, membrane permeability and unstirred layer

2023· article· en· W4387327915 on OpenAlexafffund
Farhad A. Amiri, Junfeng Zhang

Bibliographic record

VenueMedical Engineering & Physics · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneOxygen transportPermeability (electromagnetism)OxygenRed blood cellOxygen permeabilityMembrane permeabilityVolumetric flow rateChemistryBiophysicsRed CellMaterials scienceMechanicsChromatographyBiochemistryBiologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

The transport process of oxygen and other gas species across red blood cell (RBC) membrane is of great importance for better understanding the critical biological functions of RBCs, and the stopped-flow experiments have often been employed for such investigations. In previous stopped-flow analyses, the RBC had usually been represented by a spherical capsule based on the RBC volume, and an assumed unstirred layer (USL) thickness had been used to determine the membrane permeability. In this research, unlike these previous studies, we simulate the oxygen uptake process with different RBC shapes (shperical, ellipsoidal and biconcave) and examine the effects of USL thickness and membrane permeability over broad ranges based on literature values. Our results show that the excess membrane area can greatly improve the oxygen transport efficiency, and a same uptake half-time can be obtained using different combinations of USL thickness and membrane permeability. These findings raise concerns on the reliability and uncertainty for the results and conclusions in previous studies, and also call for more complete numerical models, for example, with the fluid flow and cell deformation considered, and more in-depth investigations on the oxygen transport processes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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