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Record W2022338373 · doi:10.1183/09031936.00084608

Erythropoietin inhibits respiratory epithelial cell apoptosis in a model of acute lung injury

2009· article· en· W2022338373 on OpenAlexaff
Ruth MacRedmond, Gurpreet K. Singhera, Delbert R. Dorscheid

Bibliographic record

VenueEuropean Respiratory Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity of British ColumbiaSt. Paul's HospitalProvidence Health Care
Fundersnot available
KeywordsMedicineErythropoietinApoptosisLungRespiratory systemCellCancer researchInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Fas-mediated apoptosis of the alveolar epithelium is important in the pathogenesis of acute respiratory distress syndrome. Erythropoietin (EPO) has cytoprotective properties in other organ systems, and is relatively deficient in critical illness. This study investigates a potential role for EPO in reducing apoptosis in a model of acute lung injury. Apoptosis was induced in human alveolar epithelial (A549) cells or normal human bronchial epithelial (NHBE) cells by Fas activation with CH-11 Fas-crosslinking antibody or by co-culture with polymorphonuclear neutrophils in a transwell system. The effect of recombinant human (rh)EPO on apoptosis was measured by poly(ADP-ribose) polymerase cleavage and cell death detection assay. The specific EPO-EPO receptor (EPOR)-mediated effect was determined using an EPO-blocking antibody or EPOR small interfering RNA. Expression of EPOR was demonstrated in A549, NHBE and normal human alveolar epithelium. Fas- and neutrophil-mediated apoptosis of A549 and NHBE cells was inhibited by rhEPO by a specific EPO-EPOR-mediated mechanism. This anti-apoptotic effect was associated with induction of a pro-apoptotic Bcl-xL/Bax ratio. EPO has cytoprotective properties in respiratory epithelium in an in vitro model, which may indicate a potential therapeutic role in acute lung injury.

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 categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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.

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

Citations56
Published2009
Admission routes1
Has abstractyes

Explore more

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