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Red Blood Cell Flux in Isolated Capillary Bifurcations

2009· article· en· W34164997 on OpenAlexafffund
Graham Fraser, Gemma Dias, Daniel Goldman, Christopher G. Ellis

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsBifurcationCapillary actionHematocritFlux (metallurgy)Flow (mathematics)MechanicsBlood flowRed blood cellChemistryMathematicsGeometryPhysicsThermodynamicsBiologyCardiologyNonlinear system

Abstract

fetched live from OpenAlex

Capillary red blood cell (RBC) flux is the most important determinant of oxygen delivery to tissue. Simultaneous experimental measurement of flow throughout a microvascular network is impractical and necessitates predicting flow using computer simulations. Our existing model uses an empirically derived bifurcation rule (Pries et al., 1989) to determine RBC distribution between daughter branches. Our objective is to validate the existing model of microvascular blood flow on capillary networks versus in vivo measurements. Capillary networks in rat skeletal muscle were recorded and 4 capillary bifurcations were randomly selected for analysis (diameter 5.9±0.27 ?m, segment length 107.7±28.8 ?m) . Custom vascular mapping software was used to reconstruct the real geometry of each bifurcation. Each capillary was analyzed to measure RBC velocity, tube hematocrit and RBC flux. The error in measured RBC flux in the parent compared to the sum of daughters was 1.9±8.3%. Discrete fluctuations of cell distribution over time were seen to have a significant impact on measured flux fraction (FF). Measured RBC FF in the higher flow daughters were 9±6.3% lower than the bifurcation rule predicted at equivalent flow rates. The stochastic nature of the bifurcation rule makes precise predictions of RBC distribution difficult where alternating cells paths can drastically impact measured FF. Supported by CIHR grant to DG and CGE.

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 score0.425
Threshold uncertainty score0.280

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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