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Record W1886618619 · doi:10.1039/c5lc00543d

Mechanical differences of sickle cell trait (SCT) and normal red blood cells

2015· article· en· W1886618619 on OpenAlexafffund
Yi Zheng, Mark A. Cachia, Ji Ge, Zhensong Xu, Chen Wang, Yu Sun

Bibliographic record

VenueLab on a Chip · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsMount Sinai HospitalUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSickle cell traitHemoglobin sCellBlood viscosityHemoglobinHemorheologyTraitRed blood cellShear stressSickle cell anemiaImmunologyChemistryMedicineBiologyBiophysicsPathologyMaterials scienceInternal medicineGeneticsComposite material

Abstract

fetched live from OpenAlex

Sickle cell trait (SCT) is a condition in which an individual inherits one sickle hemoglobin gene (HbS) and one normal beta hemoglobin gene (HbA). It has been hypothesized that under extreme physical stress, the compromised mechanical properties of the red blood cells (RBCs) may be the underlying mechanism of clinical complications of sickle cell trait individuals. However, whether sickle cell trait (SCT) should be treated as physiologically normal remains controversial. In this work, the mechanical properties (i.e., shear modulus and viscosity) of individual RBCs were quantified using a microsystem capable of precisely controlling the oxygen level of RBCs' microenvironment. Individual RBCs were deformed under shear stress. After the release of shear stress, the dynamic cell recovery process was captured and analyzed to extract the mechanical properties of single RBCs. The results demonstrate that RBCs from sickle cell trait individuals are inherently stiffer and more viscous than normal RBCs from healthy donors, but oxygen level variations do not alter their mechanical properties or morphology.

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 score0.045
Threshold uncertainty score0.226

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 head0.030
GPT teacher head0.225
Teacher spread0.195 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

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