Novel Strategies to Prevent Total Parenteral Nutrition‐Induced Gut and Liver Inflammation, and Adverse Metabolic Outcomes
Bibliographic record
Abstract
Total parenteral nutrition (TPN) is a life-saving therapy administered to millions of patients. However, it is associated with significant adverse effects, namely liver injury, risk of infections, and metabolic derangements. In this review, the underlying causes of TPN-associated adverse effects, specifically gut atrophy, dysbiosis of the intestinal microbiome, leakage of the epithelial barrier with bacterial invasion, and inflammation are first described. The role of the bile acid receptors farnesoid X receptor and Takeda G protein-coupled receptor, of pleiotropic hormones, and growth factors is highlighted, and the mechanisms of insulin resistance, namely the lack of insulinotropic and insulinomimetic signaling of gut-originating incretins as well as the potentially toxicity of phytosterols and pro-inflammatory fatty acids mainly released from soybean oil-based lipid emulsions, are discussed. Finally, novel approaches in the design of next generation lipid delivery systems are proposed. Propositions include modifying the physicochemical properties of lipid emulsions, the use of lipid emulsions generated from sustainable oils with favorable ratios of anti-inflammatory n-3 to pro-inflammatory n-6 fatty acids, beneficial adjuncts to TPN, and concomitant pharmacotherapies to mitigate TPN-associated adverse effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".