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How donor age affects RBC deformability in storage

2025· letter· en· W4407511924 on OpenAlexaff
Kerryn Matthews, Hongshen Ma

Bibliographic record

VenueBlood Advances · 2025
Typeletter
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsCanadian Blood ServicesVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsBlood preservationChemistryMedicineAndrology

Abstract

fetched live from OpenAlex

In this issue of Blood Advances, Mykhailova et al 1 identified that donor age, rather than donor sex, predominantly affects stored red blood cell (RBC) deformability, a crucial property for transfusion success.Specifically, this study showed that red cell concentrates (RCCs) from younger donors, especially teenage males, undergo a more rapid decline in deformability during storage than those from older donors.This finding shifts the current focus from previously assumed determinants of RBC quality, such as donor sex and RBC biological aging, to how donor age affects RCC quality and posttransfusion efficacy.By analyzing the effects of donor age on the rheological properties of stored RBCs, this study highlights the potential of using age-based donor selection to improve transfusion outcomes.RBC deformability or the ability of RBCs to alter shape and squeeze through the microvasculature is critical for oxygen delivery and overall RBC function.In transfusion medicine, improved deformability is linked to fewer posttransfusion complications and increased survival rates for transfused cells. 2 Factors affecting deformability include RBC membrane flexibility, cellular morphology, cytoplasmic viscosity, and mean corpuscular hemoglobin content (MCHC). 3During cold storage, RBCs progressively lose deformability due to metabolic degradation and membrane remodeling.This study builds on existing evidence by examining how age and sex influence these degradative processes in stored RCCs from donors at opposite ends of the age spectrum.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2025
Admission routes1
Has abstractyes

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