Cellular Signaling and Potential New Treatment Targets in Diabetic Retinopathy
Bibliographic record
Abstract
Dysfunction and death of microvascular cells and imbalance between the production and the degradation of extracellular matrix (ECM) proteins are a characteristic feature of diabetic retinopathy (DR). Glucose-induced biochemical alterations in the vascular endothelial cells may activate a cascade of signaling pathways leading to increased production of ECM proteins and cellular dysfunction/death. Chronic diabetes leads to the activation of a number of signaling proteins including protein kinase C, protein kinase B, and mitogen-activated protein kinases. These signaling cascades are activated in response to hyperglycemia-induced oxidative stress, polyol pathway, and advanced glycation end product formation among others. The aberrant signaling pathways ultimately lead to activation of transcription factors such as nuclear factor-kappaB and activating protein-1. The activity of these transcription factors is also regulated by epigenetic mechanisms through transcriptional coactivator p300. These complex signaling pathways may be involved in glucose-induced alterations of endothelial cell phenotype leading to the production of increased ECM proteins and vasoactive effector molecules causing functional and structural changes in the microvasculature. Understanding of such mechanistic pathways will help to develop future adjuvant therapies for diabetic retinopathy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".