Glucose-Induced β-Cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway
Bibliographic record
Abstract
Prolonged elevation of glucose can adversely affect β-cell function. Oxidative stress, which has been implicated in glucose-induced β-cell dysfunction, can activate c-jun N-terminal kinase (JNK). However, whether JNK is causal in glucose-induced β-cell dysfunction in vivo is unclear. Therefore, we aimed at investigating the causal role of JNK activation in in vivo models of glucose-induced β-cell dysfunction. Glucose-induced β-cell dysfunction was investigated in the presence or absence of JNK inhibition. JNK inhibition was achieved using either (i) the JNK-specific inhibitor SP600125 or (ii) JNK-1-null mice. (i) Rats or mice were infused intravenously with saline or glucose with or without SP600125. (ii) JNK-1 null mice and their littermate wild-type controls were infused intravenously with saline or glucose. Following the glucose infusion periods in rats and mice, β-cell function was assessed in isolated islets or in vivo using hyperglycemic clamps. Forty-eight-hour hyperglycemia at ~20 mM in rats or 96-hour hyperglycemia at ~13 mM in mice impaired β-cell function in isolated islets and in vivo. Inhibition of JNK using either SP600125 or JNK-1-null mice prevented glucose-induced β-cell dysfunction in isolated islets and in vivo. Islets of JNK-1-null mice exposed to hyperglycemia in vivo showed an increase in Pdx-1 and insulin 2 mRNA, whereas islets of wild-type mice did not. Together, these data show that JNK pathway is involved in glucose-induced β-cell dysfunction in vivo and is thus a potential therapeutic target for type 2 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) | 0.000 | 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 | 0.001 |
| 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".