Omega-3 Polyunsaturated Fatty Acid: A Pharmaco-Nutraceutical Approach to Improve the Responsiveness to Ursodeoxycholic Acid
Bibliographic record
Abstract
Ursodeoxycholic acid (UDCA) is the first line therapy for the treatment of cholestatic and autoimmune liver diseases. Its clinical use is currently limited by a significant proportion of non-responder patients. Polyunsaturated fatty acids (n-3 PUFAs) possess important anti-inflammatory properties and protect liver cells against bile acid (BA)-induced toxicity. The present study was designed to rapidly evaluate whether combining n-3 PUFAs (i.e., eicosapentaenoic [EPA] and docosahexaenoic [DHA] acids) to UDCA would provide additional benefits when compared to the drug alone. The parameters evaluated were (i) the expression of genes governing BA synthesis, transport, and metabolism; (ii) the prevention of BA-induced apoptosis and endoplasmic reticulum (ER)-stress; and (iii) the control of BA- and LPS-dependent inflammation. In the absence of n-3 PUFAs, most of the parameters investigated were unaffected by UDCA or were only altered by the higher dose (500 µM) of the drug. By contrast, in the presence of EPA/DHA (50/50 µM), all parameters showed a strongly improved response and the lowest UDCA dosage (50 µM) provided equal or better benefits than the highest dose used alone. For example, the combination EPA/DHA + UDCA 50 µM caused comparable down-regulation of the CYP7A1 gene expression and of the BA-induced caspase 3 activity as observed with UDCA 500 µM. In conclusion, these results suggest that the addition of n-3 PUFAs to UDCA may improve the response to the drug, and that such a pharmaco-nutraceutical approach could be used in clinic to open the narrow therapeutic dose of UDCA in cholestatic liver diseases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".