Differential activation of NF-κB and AP-1 in increased fibronectin synthesis in target organs of diabetic complications
Bibliographic record
Abstract
Increased extracellular matrix protein production leading to structural abnormalities is a characteristic feature of chronic diabetic complications. We previously showed that high glucose in endothelial cell culture leads to the upregulation of basement membrane protein fibronectin (FN) via an endothelin (ET)-dependent pathway involving activation of NF-kappaB and activating protein-1 (AP-1). To delineate the mechanisms of basement membrane thickening, we used an animal model of chronic diabetes and evaluated ET-dependent activation of NF-kappaB and AP-1 and subsequent upregulation of FN in three target organs of chronic diabetic complications. After 3 mo of diabetes, retina, renal cortex, and myocardium demonstrated increased FN mRNA and increased ET-1 mRNA expression. Increased FN expression was shown to be dependent on ET receptor-mediated signaling, as the increase was prevented by the dual ET receptor antagonist bosentan. NF-kappaB activation was most pronounced in the retina, followed by kidney and heart. AP-1 activation was also most pronounced in the retina but was similar in both kidney and heart. Bosentan treatment prevented NF-kappaB activation in the retina and heart and AP-1 activation in the retina and kidney. These data indicate that, although ETs are important in increased FN production due to diabetes, the mechanisms with respect to transcription factor activation may vary depending on the microenvironment of the organ.
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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) | 0.001 | 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".