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Impact of storage temperature and processing delays on cord blood quality: discrepancy between functional in vitro and in vivo assays

2012· article· en· W2154784709 on OpenAlexaff
Isabelle Louis, Éric Wagner, Mame Massar Dieng, Hugo Morin, Martin Champagne, Élie Haddad

Bibliographic record

VenueTransfusion · 2012
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversité LavalCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalCentre hospitalier universitaire de QuébecHéma-Québec
Fundersnot available
KeywordsCryopreservationCord bloodAndrologyHaematopoiesisIn vitroIn vivoTransplantationBiologyCD34Hematopoietic stem cell transplantationImmunologyStem cellMedicineCell biologyInternal medicineBiotechnologyEmbryoBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Optimal conditions of cord blood (CB) storage, processing, cryopreservation, and thawing are critical for banking and transplantation. Nevertheless, standardized procedures are still awaited. STUDY DESIGN AND METHODS: We evaluated the impact of preprocessing storage and temperature on recovery, viability, and functional differentiation capacities of hematopoietic progenitor cells. We compared units stored at room temperature (RT) or at 4 °C for 72 hours before cryopreservation to units processed shortly after collection (<12 hr). RESULTS: Postthaw results showed similar in vitro characteristics between immediate processing and 4 °C storage for cell recovery and viability, both significantly higher than RT storage. Surprisingly, we demonstrated that storage of CB units at RT before processing and cryopreservation profoundly altered in vivo hematopoietic reconstitution in mice, although in vitro hematopoietic colony-forming unit potential was unaltered. CONCLUSION: Our findings challenge current CB storage practices and suggest standard in vitro quality assessments may not always be indicative of CB engraftment potential.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.447

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 head0.316
Teacher spread0.286 · 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 designObservational
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

Citations29
Published2012
Admission routes1
Has abstractyes

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