IGF-I Transcript Levels in Whole-Liver Tissue, in Freshly Isolated Hepatocytes, and in Cultured Hepatocytes from Lean and Obese Zucker Rats
Bibliographic record
Abstract
BACKGROUND: The mechanisms underlying the maintenance of normal to high rates of linear growth and plasma insulin-like growth factor I (IGF-I) levels in spite of a low growth hormone secretion in obese children remain unknown. Among the animal models of early-onset obesity, obese Zucker (FA/FA) rats (which are homozygous for an inactivating missense mutation in the leptin receptor) are particularly appropriate, because their linear growth shows this growth hormone independence. METHODS: To study the regulation of IGF-I synthesis in this model, we have established primary cultures of hepatocytes derived from 12-week-old Zucker male obese and lean rats. The rat IGF-I gene contains six exons, and alternative splicing generates different mRNAs, one of which (called IGF-1B) has been shown to be decreased by fasting. We report steady state mRNA levels for IGF-I (all transcripts) and for IGF-IB in hepatocytes after 3 days in culture, in freshly isolated hepatocytes, and in whole-liver tissue. RT-PCRs using primers specific for IGF-I or IGF-IB were performed with two different internal competitors for quantification. RESULTS: In primary cultures of hepatocytes, the IGF-IB mRNA was increased by >50-fold (p = 0.01) in cells derived from obese animals as compared with cells from lean animals. However, these transcript levels were not significantly different when measured in freshly isolated hepatocytes or in whole-liver tissue. CONCLUSIONS: Increased IGF-IB transcription could be an intrinsic characteristic of cultured hepatocytes harbouring leptin receptors that bear the FA mutation. However, the modulation of this characteristic by cell-cell interactions and by in vivo hormone and metabolic status remains to be studied.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".