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Record W4411758930 · doi:10.1101/2025.06.25.661657

A Two-Phase Core-Plasma Model for Microvascular Blood Flow: Comparative Analysis of Hemodynamic Models

2025· preprint· en· W4411758930 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHemoglobin structure and function
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHemodynamicsBlood flowCore (optical fiber)Phase (matter)Flow (mathematics)CardiologyMechanicsComputer scienceMedicineChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Microcirculatory blood flow exhibits complex non-Newtonian behavior, including shear-thinning properties and the formation of a cell-free layer (CFL)—a plasma-rich region near vessel walls. While traditional rheological models such as Newtonian, Power Law, and Carreau describe certain flow characteristics, and empirical models like the double-parameter power fit have been used to capture velocity profiles, these approaches fall short in fully characterizing the dynamic interplay between red blood cells (RBCs) and plasma. This study introduces the Core-Plasma Model, a two-phase framework that integrates Newtonian and non-Newtonian elements to represent the RBC-rich core and surrounding CFL. In vitro experiments in 25 µ m and 50 µ m round channels across varying flow rates, hematocrit levels (5–20%), and suspending media (PBS and native plasma) demonstrate the model’s superior ability to capture velocity and shear rate profiles. The Core-Plasma Model offers a robust platform for advancing microscale hemodynamic predictions and deepening the understanding of microvascular flow dynamics.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.359
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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