Effects of a high-glucose environment on the pituitary growth hormone-releasing hormone receptor: type 1 diabetes compared with in vitro glucotoxicity
Bibliographic record
Abstract
The present study investigated the effects of diabetes and high glucose on GHRH receptor (GHRH-R) mRNA and protein levels in the pituitary of diabetic rats 2, 21, and 60 days post-streptozotocin (post-STZ) administration. Two days post-STZ, the 2.5-kb GHRH-R mRNA transcript was increased. Twenty-one days post-STZ, both the 2.5- and 4-kb transcripts and a 72-kDa (125)I-GHRH-GHRH-R complex were elevated. Sixty days post-STZ, the 4-kb transcript remained increased and the 45-kDa (125)I-GHRH-GHRH-R complex (functional receptor) was decreased. Hypothalamic GHRH mRNA and serum total IGF-I levels were reduced at all three time points. To better understand the role of high glucose on GHRH-R regulation, time-course effects of 33 compared with 6 mM d-glucose (DG) were examined in cultured anterior pituitary cells from 2-mo-old healthy rats. Membrane lipoperoxidation was present in 33 mM DG, and GHRH-R mRNA levels were diminished after 24 h, Fluo-GHRH internalization was marginal after 16-24 h, and GHRH-induced cAMP levels were decreased after 24 and 48 h. Altogether, these results indicate that the increase of the 2.5-kb GHRH-R mRNA transcript in vivo could be a consequence of a decrease of hypothalamic GHRH mRNA levels in STZ rats. Since it does not affect primarily functional GHRH-R levels, the initial diminution of circulating IGF-I levels could result from a decreased GHRH-R stimulation by GHRH. Thus, the effect of glucotoxicity would be related to a decrease of functional GHRH-R protein, as observed in rats 60 days post-STZ and in cultured pituitary cells from healthy rats exposed to a high-glucose environment.
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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".