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Record W3160559737 · doi:10.1038/s41598-021-89704-4

Exploitation of blood non-Newtonian properties for ultrasonic measurement of hematocrit

2021· article· en· W3160559737 on OpenAlexaff
Baptiste Pialot, Jérémie Gachelin, Jean Provost, Olivier Couture

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsPolytechnique Montréal
FundersInstitut National de la Santé et de la Recherche MédicaleAssociation Nationale de la Recherche et de la TechnologieEuropean Commission
KeywordsHematocritUltrasonic sensorBiomedical engineeringExtracorporeal circulationUltrasoundMicrofluidicsMaterials scienceMedicineComputer scienceSurgeryInternal medicineRadiologyNanotechnology

Abstract

fetched live from OpenAlex

New processing techniques for manipulating blood and its components at a microfluidic scale are currently implemented. As for extracorporeal circulation, the in-line evaluation and monitoring of blood properties during these microfluidic techniques is a challenging task. Here, we show that the blood hematocrit can be measured non-invasively in a sub-millimeter medical tube using the non-Newtonian behavior of blood velocity profile. This hematocrit measurement is demonstrated on human blood with a simple Doppler ultrasound system. Results show a mean measurement error of 4.6 ± 1.3%Hct for hematocrit up to 52% and for 5 s-long ultrasonic signals. The simplicity and the measurement scale of the approach make it highly valuable for measuring hematocrit in new blood separation techniques. The approach may have an impact on in-vitro blood processing in general.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.031
GPT teacher head0.211
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2021
Admission routes1
Has abstractyes

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