MétaCan
Menu
Back to cohort
Record W1608560430 · doi:10.1016/j.stemcr.2015.05.003

Bmi-1 Regulates Extensive Erythroid Self-Renewal

2015· article· en· W1608560430 on OpenAlexaff
Ah Ram Kim, Jayme Olsen, Samantha J. England, Yu‐Shan Huang, Katherine H. Fegan, Luis F. Delgadillo, Kathleen E. McGrath, Paul D. Kingsley, Richard E. Waugh, James Palis

Bibliographic record

VenueStem Cell Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood InstituteMedical Center, University of RochesterNational Institutes of HealthNew York State Stem Cell Science
KeywordsBiologyCell biologyBone marrowStem cellIn vivoPopulationImmunologyEmbryoErythropoiesisIn vitroErythroblastEx vivoHaematopoiesisGeneticsAnemiaInternal medicine

Abstract

fetched live from OpenAlex

Red blood cells (RBCs), responsible for oxygen delivery and carbon dioxide exchange, are essential for our well-being. Alternative RBC sources are needed to meet the increased demand for RBC transfusions projected to occur as our population ages. We previously have discovered that erythroblasts derived from the early mouse embryo can self-renew extensively ex vivo for many months. To better understand the mechanisms regulating extensive erythroid self-renewal, global gene expression data sets from self-renewing and differentiating erythroblasts were analyzed and revealed the differential expression of Bmi-1. Bmi-1 overexpression conferred extensive self-renewal capacity upon adult bone-marrow-derived self-renewing erythroblasts, which normally have limited proliferative potential. Importantly, Bmi-1 transduction did not interfere with the ability of extensively self-renewing erythroblasts (ESREs) to terminally mature either in vitro or in vivo. Bmi-1-induced ESREs can serve to generate in vitro models of erythroid-intrinsic disorders and ultimately may serve as a source of cultured RBCs for transfusion therapy.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.643

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 head0.023
GPT teacher head0.249
Teacher spread0.226 · 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 designNot applicable
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

Citations20
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueStem Cell ReportsSame topicErythrocyte Function and PathophysiologyFrench-language works237,207