Hydrophilic bile acids prevent liver damage caused by lack of biliary phospholipid in Mdr2 mice
Bibliographic record
Abstract
Bile acid imbalance causes progressive familial intrahepatic cholestasis type 2 (PFIC2) or type 3 (PFIC3), severe liver diseases associated with genetic defects in the biliary bile acid transporter bile salt export pump (BSEP; ABCB11) or phosphatidylcholine transporter multidrug resistance protein 3 (MDR3; ABCB4), respectively. Mdr2 −/− mice (a PFIC3 model) develop progressive cholangitis, ductular proliferation, periportal fibrosis, and hepatocellular carcinoma (HCC) because the nonmicelle-bound bile acids in the bile of these mice are toxic. We asked whether the highly hydrophilic bile acids generated by Bsep −/− mice could protect Mdr2 −/− mice from progressive liver damage. We generated double-KO (DKO: Bsep −/− and Mdr2 −/− ) mice. Their bile acid composition resembles that of Bsep −/− mice, with increased hydrophilic muricholic acids, tetrahydroxylated bile acids (THBAs), and reduced hydrophobic cholic acid. These mice lack the liver pathology of their Mdr2 −/− littermates. The livers of DKO mice have gene expression profiles very similar to Bsep −/− mice, with 4,410 of 6,134 gene expression changes associated with the Mdr2 −/− mutation being suppressed. Feeding with THBAs partially alleviates liver damage in the Mdr2 −/− mice. Hydrophilic changes to biliary bile acid composition, including introduction of THBA, can prevent the progressive liver pathology associated with the Mdr2 −/− (PFIC3) mutation.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".