Triglyceride Uptake and Lipoprotein Lipase–Generated Fatty Acid Spillover in the Splanchnic Bed of Dogs
Bibliographic record
Abstract
The action of lipoprotein lipase on triglyceride-rich lipoproteins generates fatty acids that are either transported into tissues or mix with circulating free fatty acids (FFAs) via a process known as spillover. In the present study, arterial, portal vein, and hepatic vein sampling catheters were surgically placed in nine mongrel dogs. The animals were subsequently studied after a 42-h fast during infusion of [14C]oleate and a lipid emulsion containing [3H]triolein; the emulsion was used as a surrogate for the study of chylomicron metabolism. More than one-half of splanchnic [3H]triglyceride uptake occurred in the liver, and substantial fractional spillover of [3H]oleate was observed in both liver and nonhepatic tissues (approximately 50% each). There was a significant correlation between FFA release from nonhepatic tissues (presumably visceral fat) and nonhepatic fractional spillover (R = 0.81, P < 0.01), consistent with a model in which the rate of intracellular lipolysis influences spillover by determining the direction of net fatty acid flow between the cell and the interstitium. There was a significant correlation between "true" and "net" splanchnic spillover (R = 0.84, P < 0.005), the latter representing calculation of spillover between arterial and hepatic venous blood without portal venous data. Metabolism of chylomicron triglycerides in visceral fat may be an important source of portal venous FFAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".