MétaCan
Menu
Back to cohort
Record W4399055605 · doi:10.3390/antiox13060646

The microRNA Let-7f Induces Senescence and Exacerbates Oxidative Stress in Retinal Pigment Epithelial Cells

2024· article· en· W4399055605 on OpenAlexafffund
Christina Ortiz, Houda Tahiri, Chun Yang, Claudia Gilbert, Carl Fortin, Pierre Hardy

Bibliographic record

VenueAntioxidants · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
FundersNational Institutes of HealthUniversité de Montréal
KeywordsOxidative stressSenescenceRetinal pigment epitheliumCell biologyReactive oxygen speciesRetinalBiologyChemistryEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

This study aims to investigate the role of microRNA let-7f in the dysfunction and degeneration of retinal pigment epithelium (RPE) cells through the induction of senescence and oxidative stress. Furthermore, we explore whether let-7f inhibition can protect these cells against sodium iodate (SI)-induced oxidative stress. Oxidative stress and let-7f expression are reciprocally regulated in retinal pigment epithelial cells. Overexpression of let-7f in ARPE-19 cells induced oxidative stress as demonstrated by increased reactive oxygen species (ROS) production as well as senescence. Inhibition of let-7f successfully protected RPE cells from the detrimental effects induced by SI. In addition, let-7f overexpression induced RPE cellular dysfunction by diminishing their migratory capabilities and reducing the phagocytosis of porcine photoreceptor outer segments (POS). Results were further confirmed in vivo by intravitreal injections of SI and let-7f antagomir in C57BL/6 mice. Our results provide strong evidence that let-7f is implicated in the dysfunction of RPE cells through the induction of senescence and oxidative injury. These findings may help to uncover novel and relevant processes in the pathogenesis of dry AMD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.411

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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAntioxidantsSame topicRetinal Diseases and TreatmentsFrench-language works237,207