Hyperinsulinemic euglycemic step clamping with tracer glucose infusion and labeled glucose infusate for assessment of acute insulin resistance in pigs
Bibliographic record
Abstract
The present study aimed to establish hyperinsulinemic euglycemic step clamping with tracer glucose infusion and labeled glucose infusate (step hot-GINF HEC) for assessment of acute insulin resistance in anesthetized pigs and to arrange for combination with invasive investigative methods. Tracer enrichment was measured during D-[6,6-(2)H(2)]glucose infusion before and after surgical instrumentation (n = 8). Insulin dose-response characteristics were determined by two step hot-GINF HEC procedures, with accordingly labeled glucose infusates performed at a total of six insulin infusion rates ranging from 0.2 to 2.0 mU kg(-1) min(-1) (n = 8). Finally, three-step hot-GINF HEC (0.4, 1.2, and 2.0 mU kg(-1) min(-1)) was performed subsequent to major surgical trauma (n = 8). Tracer enrichment, basal glucose kinetics, and circulating levels of C-peptide, cortisol, glucagon, and catecholamines were not influenced by surgical instrumentation. Mean intraindividual coefficient of variance levels for glucose infusion rates and repeatedly measured insulin, glucose, and tracer enrichment indicated stable clamping conditions. Basal and maximal insulin-stimulated glucose utilization was twice as high as in humans at approximately 5.5 and 21 mg kg(-1) min(-1). Surgical trauma elicited pronounced peripheral and moderate hepatic insulin unresponsiveness (45% lower whole body glucose disposal and 19% less suppressed endogenous glucose release) and apparently diminished metabolic insulin clearance. Step hot-GINF HEC seems suitable for assessment of acute insulin resistance in anesthetized pigs, and combination with invasive investigative methods requiring surgical instrumentation can be accomplished without the premises for utilization of the technique being altered, but attention must be paid to alterations in metabolic insulin clearance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".