ABC Transporters, Bile Acids, and Inflammatory Stress in Liver Cancer
Bibliographic record
Abstract
The biliary secretion of bile acids is critical for multiple liver functions including digesting fatty nutrients and driving bile flow. When this process is impaired, the accumulating bile acids cause inflammatory liver injury. Multiple ABC transporters in the liver are key players to safeguard the hepatocyte and avoid toxicity due to bile acid over-accumulation. BSEP provides for efficient secretion of bile acids across the canalicular membrane against a steep concentration gradient. MDR3/Mdr2 and ABCG5/G8 secrete phosphatidylcholine and cholesterol, respectively, in coordination with BSEP-mediated bile acid secretion to mask the detergent/toxic effects of bile acids in the bile ductular space. Several lines of evidence indicate that when these critical steps are compromised, bile acid toxicity in vivo leads to inflammatory liver injury and liver cancer. In bsep-/- mice, liver cancer is rare. These mice display greatly increased expression of alternative bile acid transporters, such as Mdr1a/1b, Mrp3 and Mrp4. We believe these alternative transport systems provide an additional safeguard to avoid bile acid overload in liver. Such backup systems appear to be under-utilized in humans, as defects in BSEP and MDR3 lead to severe, often fatal childhood diseases. It is possible, therefore, that targeting ABC transporters and modulating the toxicity of the bile acid pool could be vital interventions to alleviate chronic inflammation and reduce the incidence of liver cancer in high-risk populations. The combination of an alternative ABC transporter with a novel substrate may prove an effective chemo-preventive or therapeutic strategy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".