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Record W2031683385 · doi:10.1039/c2lc21210b

High-throughput biophysical measurement of human red blood cells

2012· article· en· W2031683385 on OpenAlexafffund
Yi Zheng, Ehsan Shojaei-Baghini, Azar Azad, Chen Wang, Yu Sun

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofluidicsBiomedical engineeringElectrical impedanceRed blood cellMaterials scienceThroughputChemistryNanotechnologyMedicineComputer scienceBiochemistryElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper reports a microfluidic system for biophysical characterization of red blood cells (RBCs) at a speed of 100-150 cells s(-1). Electrical impedance measurement is made when single RBCs flow through a constriction channel that is marginally smaller than RBCs' diameters. The multiple parameters quantified as mechanical and electrical signatures of each RBC include transit time, impedance amplitude ratio, and impedance phase increase. Histograms, compiled from 84,073 adult RBCs (from 5 adult blood samples) and 82,253 neonatal RBCs (from 5 newborn blood samples), reveal different biophysical properties across samples and between the adult and neonatal RBC populations. In comparison with previously reported microfluidic devices for single RBC biophysical measurement, this system has a higher throughput, higher signal to noise ratio, and the capability of performing multi-parameter measurements.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.041
GPT teacher head0.270
Teacher spread0.229 · 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
GenreMethods

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

Citations156
Published2012
Admission routes2
Has abstractyes

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