Choice of Lipid Emulsion Determines Inflammation of the Gut‐Liver Axis, Incretin Profile, and Insulin Signaling in a Murine Model of Total Parenteral Nutrition
Bibliographic record
Abstract
SCOPE: The aim of this study is to test whether the choice of the lipid emulsion in total parenteral nutrition (TPN), that is, n-3 fatty acid-based Omegaven versus n-6 fatty acid-based Intralipid, determines inflammation in the liver, the incretin profile, and insulin resistance. METHODS AND RESULTS: Jugular vein catheters (JVC) are placed in C57BL/6 mice and used for TPN for 7 days. Mice are randomized into a saline group (saline infusion with oral chow), an Intralipid group (IL-TPN, no chow), an Omegaven group (OV-TPN, no chow), or a chow only group (without JVC). Both TPN elicite higher abundance of lipopolysaccharide binding protein in the liver, but only IL-TPN increases interleukin-6 and interferon-γ, while OV-TPN reduces interleukin-4, monocyte chemoattractant protein-1, and interleukin-1α. Insulin plasma concentrations are higher in both TPN, while glucagon and glucagon-like peptide-1 (GLP-1) were higher in IL-TPN. Gluconeogenesis is increased in IL-TPN and the nuclear profile of key metabolic transcription factors shows a liver-protective phenotype in OV-TPN. OV-TPN increases insulin sensitivity in the liver and skeletal muscle. CONCLUSION: OV-TPN as opposed to IL-TPN mitigates inflammation in the liver and reduces the negative metabolic effects of hyperinsulinemia and hyperglucagonemia by "re-sensitizing" the liver and skeletal muscle to insulin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".