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Record W3031867929 · doi:10.3390/metabo10060226

Restoration of Physiological Levels of Uric Acid and Ascorbic Acid Reroutes the Metabolism of Stored Red Blood Cells

2020· article· en· W3031867929 on OpenAlexaff
Manon Bardyn, Jingkui Chen, Michaël Dussiot, David Crettaz, L Schmid, Emmanuel Längst, Pascal Amireault, Jean‐Daniel Tissot, Mario Jolicœur, Michel Prudent

Bibliographic record

VenueMetabolites · 2020
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsPolytechnique Montréal
FundersAgence Nationale de la Recherche
KeywordsAscorbic acidUric acidAntioxidantMetabolismChemistryMetaboliteBiochemistryOxidative stressRed blood cellFood scienceAndrologyMedicine

Abstract

fetched live from OpenAlex

After blood donation, the red blood cells (RBCs) for transfusion are generally isolated by centrifugation and then filtrated and supplemented with additive solution. The consecutive changes of the extracellular environment participate to the occurrence of storage lesions. In this study, the hypothesis is that restoring physiological levels of uric and ascorbic acids (major plasmatic antioxidants) might correct metabolism defects and protect RBCs from the very beginning of the storage period, to maintain their quality. Leukoreduced CPD-SAGM RBC concentrates were supplemented with 416 µM uric acid and 114 µM ascorbic acid and stored during six weeks at 4 °C. Different markers, i.e., haematological parameters, metabolism, sensitivity to oxidative stress, morphology and haemolysis were analyzed. Quantitative metabolomic analysis of targeted intracellular metabolites demonstrated a direct modification of several metabolite levels following antioxidant supplementation. No significant differences were observed for the other markers. In conclusion, the results obtained show that uric and ascorbic acids supplementation partially prevented the metabolic shift triggered by plasma depletion that occurs during the RBC concentrate preparation. The treatment directly and indirectly sustains the antioxidant protective system of the stored 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.394

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 integrity0.0000.000
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 head0.048
GPT teacher head0.261
Teacher spread0.214 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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