Metabolic effects of overnight continuous infusion of unacylated ghrelin in humans
Bibliographic record
Abstract
OBJECTIVE: To clarify the metabolic effects of an overnight i.v. infusion of unacylated ghrelin (UAG) in humans. UAG exerts relevant metabolic actions, likely mediated by a still unknown ghrelin receptor subtype, including effects on β-cell viability and function, insulin secretion and sensitivity, and glucose and lipid metabolism. DESIGN: We studied the effects of a 16-h infusion (from 2100 to 1300 h) of UAG (1.0 μg/kg per h) or saline in eight normal subjects (age (mean±s.e.m.), 29.6±2.4 years; body mass index (BMI), 22.4±1.7 kg/m(2)), who were served, at 2100 and 0800 h respectively, with isocaloric balanced dinner and breakfast. Glucose, insulin, and free fatty acid (FFA) levels were measured every 20 min. RESULTS: In comparison with saline, UAG induced significant (P<0.05) changes in glucose, insulin, and FFA profiles. UAG infusion decreased glucose area under the curve (AUC) values by 10% (UAG(0 - 960 min): 79.0±1.7×10(3) mg/dl per min vs saline(0- 960 min): 87.5±3.8×10(3) mg/dl per min) and the AUC at night by 14% (UAG(180)(-)(660 min): 28.4±0.5×10(3) mg/dl per min vs saline(180 - 660 min): 33.2±1.1×10(3) mg/dl per min). The overall insulin AUC was not significantly modified by UAG infusion; however, insulin AUC observed after meals was significantly increased under the exposure to UAG with respect to saline at either dinner or breakfast. The FFA AUC values were decreased by 52% under the exposure to UAG in comparison with saline (UAG(0 - 960 min): 0.3±0.02×10(3) mEq/l per min vs saline(0 - 960 min): 0.6±0.05×10(3) mEq/l per min). CONCLUSIONS: Exposure to the i.v. administration of UAG improves glucose metabolism and inhibits lipolysis in healthy volunteers. Thus, in contrast to the diabetogenic action of AG, UAG displays hypoglycemic properties.
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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".