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Record W2015273849 · doi:10.1182/blood-2003-10-3705

Modulating erythrocyte chimerism in a mouse model of pyruvate kinase deficiency

2004· article· en· W2015273849 on OpenAlexaff
Robert E. Richard, Michael Weinreich, Kai‐Hsin Chang, Jessica Ieremia, Mary M. Stevenson, C. Anthony Blau

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsMontreal General Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsPyruvate kinase deficiencyPyruvate kinaseHaematopoiesisIn vivoErythropoietinTransplantationBiologyRed blood cellImmunologyHemolytic anemiaChemistryCancer researchCell biologyBiochemistryEndocrinologyInternal medicineMedicineStem cellGlycolysisEnzymeGenetics

Abstract

fetched live from OpenAlex

In vivo selection may provide a means to increase the relative number of cells of donor origin in recipients with hemopoietic chimerism. We have tested whether in vivo selection using chemical inducers of dimerization (CIDs) can direct the expansion of transduced normal donor erythrocytes in recipients with chimerism using a mouse model of pyruvate kinase deficiency. Marrow cells from normal CBA/N mice were transduced with a vector (F36Vmpl(GFP)) that promotes cell growth in the presence of CIDs. Transduced cells were then transplanted into minimally conditioned, pyruvate kinase-deficient recipients (CBA-Pk-1(slc)/Pk-1(slc)) to establish stable chimerism. CID administration resulted in expansion of normal donor erythrocytes and improvement of the anemia. The preferential expansion of normal erythrocytes also resulted in a decrease in erythropoietin levels, reducing the drive for production of pyruvate kinase-deficient red blood cells. CID-mediated expansion of genetically modified erythrocytes could prove a useful adjunct to transplantation methods that achieve erythroid chimerism.

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 score0.033
Threshold uncertainty score0.453

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 head0.022
GPT teacher head0.252
Teacher spread0.229 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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