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Record W2105792913 · doi:10.1084/jem.20100665

Erythropoietin protects against diabetes through direct effects on pancreatic β cells

2010· article· en· W2105792913 on OpenAlexafffund
Diana Choi, Stephanie A. Schroer, Shun Lu, Linyuan Wang, Xiaohong Wu, Yunfeng Liu, Yi Zhang, Herbert Y. Gaisano, Kay‐Uwe Wagner, Hong Wu, Ravi Retnakaran, Minna Woo

Bibliographic record

VenueThe Journal of Experimental Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsSt. Michael's HospitalMount Sinai HospitalUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsErythropoietinDiabetes mellitusPancreatic isletsEndocrinologyStreptozotocinType 2 diabetesMedicineErythropoietin receptorInternal medicineImmunologyCancer researchIslet

Abstract

fetched live from OpenAlex

A common feature among all forms of diabetes mellitus is a functional β-cell mass insufficient to maintain euglycemia; therefore, the promotion of β-cell growth and survival is a fundamental goal for diabetes prevention and treatment. Evidence has suggested that erythropoietin (EPO) exerts cytoprotective effects on nonerythroid cells. However, the influence of EPO on pancreatic β cells and diabetes has not been evaluated to date. In this study, we report that recombinant human EPO treatment can protect against diabetes development in streptozotocin-induced and db/db mouse models of type 1 and type 2 diabetes, respectively. EPO exerts antiapoptotic, proliferative, antiinflammatory, and angiogenic effects within the islets. Using β-cell-specific EPO receptor and JAK2 knockout mice, we show that these effects of EPO result from direct biological effects on β cells and that JAK2 is an essential intracellular mediator. Thus, promotion of EPO signaling in β cells may be a novel therapeutic strategy for diabetes prevention and treatment.

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 score0.020
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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

Citations127
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Experimental MedicineSame topicErythropoietin and Anemia TreatmentFrench-language works237,207