Oxidative-stress related increase in keratoconus tear MDA and GPX3 while NRF2-antioxidant functions decrease in stromal cells
Bibliographic record
Abstract
Keratoconus (KC) is a common eye disease where the cornea undergoes degenerative thinning and steepening. The absence of biomarkers for early diagnosis prior to the onset of overt corneal phenotypes and the lack of curative treatments rooted in a fundamental understanding of KC biology remain significant challenges. To address these issues, we investigated the role of unresolved oxidative stress in KC pathogenesis. Malondialdehyde (MDA) a lipid peroxidation byproduct that accumulates during oxidative stress was significantly elevated in the tears of KC patients compared to unaffected controls and positively correlated with maximal keratometry (Kmax), a measure of KC severity. Similarly, the secreted antioxidant glutathione peroxidase 3 (GPX3), was significantly increased in patient tears, and strongly correlated with Kmax. In a cell culture model of oxidative stress, KC corneal stromal cells displayed increased apoptosis and suboptimal activation of NRF2, a transcription factor master regulator of antioxidant genes. Conversely, inhibition of NRF2 in donor stromal cells elicited KC-like cellular phenotype, whereas sulforaphane, an NRF2 booster restored antioxidant gene expression and the deposition of cornea-typical collagens. Our study identified cellular antioxidant signaling dysregulations in keratoconus where sulforaphane treatment may be restorative. Consistent increases in patient tear MDA and GPX3 present these as promising biomarkers for KC diagnosis and severity predictions.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".