Relationship between aldose reductase enzyme and the signaling pathway of protein kinase C in an in vitro diabetic retinopathy model
Bibliographic record
Abstract
Protein kinase C (PKC) and aldose reductase (AR) enzyme activities are increased in diabetes and complications are include retinopathy, nephropathy, and neuropathy. However, the relationship between PKC and AR and the underlying molecular mechanisms is still unclear. We aimed to evaluate the relationship between these two enzymes and clarify the underlying molecular mechanisms by the related signaling molecules. The effects of hyperglycemia and oxidative stress on AR and PKC enzymes and the signaling molecules such as nuclear factor-kappa B (NF-κB), inhibitor kappa B-alpha (IkB-α), total c-Jun, phospho c-Jun, and stress-activated protein kinases (SAPK)/Jun amino-terminal kinases (JNK) were evaluated in human retinal pigment epithelial cells (ARPE-19). AR, PKC protein levels, and related signaling molecules increased with hyperglycemia and oxidative stress. The AR inhibitor sorbinil decreased PKC expression and activity and all signaling molecule protein levels. Increased AR expression during hyperglycemia and oxidative stress was found to be correlated with the increase in PKC expression and activity in both conditions. Decreased expression and activity of PKC and the protein levels of related signaling molecules with the AR inhibitor sorbinil showed that AR enzyme may play a key role in the expression of PKC enzyme and oxidative stress during diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".