Bye, bye, bile: how altered bile acid composition changes small intestinal lipid sensing
Bibliographic record
Abstract
The gastrointestinal (GI) tract is increasingly recognised as a major contributor to energy homoeostasis that impact obesity progression. The gut represents the first site of interaction between incoming nutrients and the host, generating crucial negative feedback signalling to regulate food intake possibly by altering GI function like gastric emptying. In the case of proximal intestinal lipid sensing, several mechanisms have been identified to drive both satiety and satiation. For example, dietary fat is hydrolysed and absorbed into enterocytes. In the upper GI, this stimulates the synthesis of oleoylethanolamide (OEA), which can then act as a signalling molecule to induce satiety via activation of peroxisome proliferator-activated receptor-α and a gut-brain axis.1 Alternatively, activation of enteroendocrine cells (EECs) by free fatty acids binding to G protein-coupled receptor-40 leads to secretion of gut peptides, like cholecystokinin and glucagon-like peptide-1 (GLP-1), which slow gastric emptying and reduce food intake. Interestingly, activation of EECs via free fatty acids is hypothesised to occur on the basolateral side, requiring chylomicron formation, and thus dietary fat hydrolysis, similar to OEA production.2 Given that bile acids, especially cholic acid in mice, emulsify dietary lipids and thus promote efficient hydrolysis and absorption of lipids in the small intestine, their function would implicate a necessity in activating the aforementioned pathways to lower food intake. In GUT , Higuchi et al observed that Cyp8b1-/- mice exhibited reduced body weight and adiposity due to an inhibition of food intake. As expected, lowering cholic acid and other 12α-hydroxylated bile acids via …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".