The microRNA Let-7f Induces Senescence and Exacerbates Oxidative Stress in Retinal Pigment Epithelial Cells
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".