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Record W4323567069 · doi:10.3389/fphys.2023.1127103

The impact of biological age of red blood cell on in vitro endothelial activation markers

2023· article· en· W4323567069 on OpenAlexaff
Abdulrahman Alshalani, Boukje M. Beuger, Anita M. Tuip‐de Boer, Robin van Bruggen, Jason P. Acker, Nicole P. Juffermans

Bibliographic record

VenueFrontiers in Physiology · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
FundersDeanship of Scientific Research, King Saud UniversityKing Saud University
KeywordsPercollRed blood cellImmunologyAndrologyEndotheliumEndothelial activationEndothelial stem cellStimulationImmune systemWhole bloodBiologyCytokineIn vitroMedicineInflammationEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Introduction: Blood donor characteristics influence red blood cell transfusion outcomes. As donor sex affects the distribution of young to old RBCs in the circulation, we hypothesized that the amount of circulating young RBCs in the blood product are associated with immune suppression. Materials and Methods: Blood samples were collected from healthy volunteers and density fractionated into young and old subpopulations. In an activated endothelial cell model, RBC adhesion to endothelium and secretion of endothelial activation markers were assessed. The impact of RBC biological age was also assessed in a T cell proliferation assay and in a whole blood stimulation assay. Results: After Percoll fractionation, young RBCs contained more reticulocytes compared to old RBCs. Young RBCs associated with lower levels of E-selectin, ICAM-1, and vWF from activated endothelial cells compared to old RBCs. RBC subpopulations did not affect T cell proliferation or cytokine responses following whole blood stimulation. Conclusion: Young RBCs contain more reticulocytes which are associated with lower levels of endothelial activation markers compared to old RBCs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.222

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 head0.014
GPT teacher head0.256
Teacher spread0.243 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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