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Record W2916149997 · doi:10.1111/trf.15220

An objective flow cytometry method to rapidly determine cord blood potency in cryopreserved units

2019· article· en· W2916149997 on OpenAlexaff
Carl Simard, Guillaume Bonnaure, Diane Fournier, Sonia Néron

Bibliographic record

VenueTransfusion · 2019
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversité LavalHéma-Québec
Fundersnot available
KeywordsCryopreservationCord bloodFlow cytometryAndrologyPotencyCD34ChemistryImmunologyBiologyMedicineStem cellIn vitroBiochemistryCell biology

Abstract

fetched live from OpenAlex

BACKGROUND: Cord blood banks have to determine the regenerative potential of cord blood units (CBUs) on a representative sample of the cryopreserved product before release to the transplant center. Potency can be measured by using a colony-forming unit (CFU) method, which delays the release of CBU by 7 to 14 days. To accelerate CBU qualification, we have developed a rapid method to assess the response of CD34 cells to interleukin (IL)-3. Flow cytometry was used to measure IL-3-induced STAT5 phosphorylation within CD34-cells. This IL-3 test was compared to the CFU method, as well as the aldehyde dehydrogenase (ALDH) enzyme-based assay. STUDY DESIGN AND METHODS: cells, and IL-3-responsive CD34+ cells. Extreme and mild warming event scenarios were simulated on CBUs and used as poor-quality samples. Segments, tubes, and bags from five CBUs were compared for their potency using IL-3 and CFU methods. RESULTS: The IL-3 test was accurate in identifying the samples handled following standard operating procedures and those subjected to extreme warming events. Based on these results, a threshold of 55% of IL-3-responsive CD34 cells was established to identify good-quality samples. The IL-3 test was also the most sensitive to detect samples subjected to milder warming events. CONCLUSIONS: Our new method for determining CBU functionality is rapid, unbiased, and robust. The IL-3 test described herein fulfills the requirements for validation, and we intend to implement this method in our cord blood bank facility.

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)0.001
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 score0.301
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.307
Teacher spread0.285 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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