A Model of Chronic Nutrient Infusion in the Rat
Bibliographic record
Abstract
Chronic exposure to excessive levels of nutrients is postulated to affect the function of several organs and tissues and to contribute to the development of the many complications associated with obesity and the metabolic syndrome, including type 2 diabetes. To study the mechanisms by which excessive levels of glucose and fatty acids affect the pancreatic beta-cell and the secretion of insulin, we have established a chronic nutrient infusion model in the rat. The procedure consists of catheterizing the right jugular vein and left carotid artery under general anesthesia; allowing a 7-day recuperation period; connecting the catheters to the pumps using a swivel and counterweight system that enables the animal to move freely in the cage; and infusing glucose and/or Intralipid (a soybean oil emulsion which generates a mixture of approximately 80% unsaturated/20% saturated fatty acids when infused with heparin) for 72 hr. This model offers several advantages, including the possibility to finely modulate the target levels of circulating glucose and fatty acids; the option to co-infuse pharmacological compounds; and the relatively short time frame as opposed to dietary models. It can be used to examine the mechanisms of nutrient-induced dysfunction in a variety of organs and to test the effectiveness of drugs in this context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".