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Record W3008908699 · doi:10.21037/aes.2019.ab038

AB038. Pathological neovascularization in retinopathy of prematurity is regulated by heme-derived iron trafficking

2019· article· en· W3008908699 on OpenAlexaff
Tapan Agnihotri, Nicholas Kim, Gaël Cagnone, Jin Sung Kim, Émilie Heckel, Sheetal Pundir, Perrine Gaub, Florian Wünnemann, Walter A. Szarek, Hyman M. Schipper, Jean‐Sébastien Joyal

Bibliographic record

VenueAnnals of Eye Science · 2019
Typearticle
Languageen
FieldMedicine
TopicRetinopathy of Prematurity Studies
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsRetinopathy of prematurityPathologicalNeovascularizationMedicineHemeOphthalmologyPathologyChemistryCancer researchAngiogenesisBiologyBiochemistryPregnancyGeneticsGestational age

Abstract

fetched live from OpenAlex

Background: Retinopathy of prematurity (ROP) is an eye disease of the immature newborn characterized by pathological neovascularization (NV). ROP classically arises due to changes in oxygen availability in the retina. However, other factors, such as red blood cell (RBC) transfusion, independently contribute to disease severity. Heme molecules, rich in RBC, are the primary source of endogenous iron. Heme is metabolized by heme oxygenase (HO) into biliverdin, carbon monoxide, and ferrous ions. Low iron levels stabilize hypoxia-inducible factor 1α (HIF1 α), the main transcription factor of vascular endothelial growth factor (VEGF) that drives angiogenesis. Here we investigate the role of heme metabolism in pathological NV. Methods: ROP was studied using the well-characterized oxygen-induced retinopathy (OIR) mouse model. Wild-type (WT) pups are exposed to high oxygen concentrations (FiO2 75%) for 5 days, from the post-natal day (P) 7 to 12, and subsequently returned to room air until retinal collection at P12, P14, & P17. Retinas are then analyzed via single-cell RNAseq, RT-qPCR, western blot, Prussian blue staining, and immunostaining techniques to elucidate the effect of OIR on iron metabolism and Hmox1. In OIR, we quantified vaso-obliteration (VO) and pathologic neovascular (NV) areas at P17 to assess the effects of1 Hmox1 competitive inhibition, and2Hmox1 allosteric inhibition. Results: Iron trafficking genes across different retinal cell-types were upregulated in OIR, including CP, FTH1, IREB2, TF, and Hmox1. Moreover, Prussian blue staining suggests iron accumulation in retinal vessels exposed to OIR. Hmox1 mRNA (n=7, P<0.01 at P17) and protein expression (n=3, P<0.05) were increased 3.8-fold from P12 to 19. Immunostaining and single-cell RNAseq confirmed that Hmox1 predominantly resides in retinal microglial cells. Competitively and allosterically Hmox1 inhibition decreased NV by 15% (n=15, P=0.02) and 60% (n=5, P<0.01) respectively. Conclusions: Iron metabolism has seldom been explored in the context of ROP. Perhaps microglial heme metabolism by Hmox1 contributes to HIF1α stabilization and pathological NV.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.027
GPT teacher head0.315
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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