Preventative effects of prostaglandin E1 in combination with iodized olive oil on liver fibrosis after transcatheter arterial chemoembolization in a rabbit model of CCl<sub>4</sub>-induced liver fibrosis
Bibliographic record
Abstract
To explore the preventative effects of prostaglandin E1 (PGE1) on a rabbit model of CCl4-induced liver fibrosis after transcatheter arterial chemoembolization (TACE), we generated a rabbit model of CCl4-induced liver fibrosis by treatment with 40% CCl4 in iodized olive oil for 16 weeks. Body mass and serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), albumin (ALB), albumin:globulin ratio (A:G), total bilirubin (TBIL), and direct bilirubin (DBIL) were measured. After TACE, the levels of hyaluronic acid (HA), procollagen III (PC III), laminin (LN), and collagen IV (IV-C) were measured, and the severity of liver fibrosis as well as the morphology of liver tissues were determined. Body mass in the model group was significantly decreased from 10 to 16 weeks, and the serum levels of ALT, AST, TP, TBIL, and DBIL levels were significantly increased while the model was being generated; the levels of ALB and A:G were significantly decreased. After TACE, serum levels of HA, PC III, and LN in the group injected with 1.0 mL iodized olive oil (Group B) were higher than in the group that were injected with 1.0 mL iodized olive oil + 0.2 mL PGE1 (Group C), whereas the serum levels of IV-C were lower. The severity of liver fibrosis was ameliorated in Group C. The combination of PGE1 and iodized olive oil prevented the development of liver fibrosis following TACE.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".