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Record W2555077203 · doi:10.1038/micronano.2016.61

Stiffening of sickle cell trait red blood cells under simulated strenuous exercise conditions

2016· article· en· W2555077203 on OpenAlexafffund
Zhensong Xu, Yi Zheng, Xian Wang, Nadine Shehata, Chen Wang, Shaorong Xie, Yu Sun

Bibliographic record

VenueMicrosystems & Nanoengineering · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsStiffeningSickle cell traitAthletesRed blood cellMedicineCardiologyInternal medicineDeoxygenationChemistryPhysical therapyBiochemistryMaterials scienceDisease

Abstract

fetched live from OpenAlex

The higher risk of vaso-occlusion events and sudden death for sickle-cell trait (SCT) athletes has been speculatively ascribed to SCT red blood cell (RBC) stiffening during strenuous exercise. However, the microenvironmental changes that could induce the stiffening of SCT RBCs are unknown. To address this question, we measured the mechanical properties of and changes in SCT RBCs under deoxygenated and acidic environments, which are two typical conditions present in the circulation of athletes undertaking strenuous exercise. The results reveal that SCT RBCs are inherently stiffer than RBCs from non-SCT healthy subjects, and a lower pH further stiffens the SCT cells. Furthermore, at both normal and low pH levels, deoxygenation was found to not be the cause of the stiffness of SCT RBCs. This study confirms that the stiffening of SCT RBCs occurs at a low pH and implies that SCT RBC stiffening could be responsible for vaso-occlusion in SCT athletes during strenuous exercise.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.198
Teacher spread0.191 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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