Hepatoprotective Effects of Aureobasidium pullulans Derived β 1,3–1,6 Glucans in a Murine Model of Non-alcoholic Steatohepatitis
Bibliographic record
Abstract
Background Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are highly prevalent conditions characterized by inflammation and fibrosis of the liver, which can progress to cirrhosis and hepatocellular carcinoma if left untreated. Conventional modalities are mainly symptomatic, with no definite solution. Beta-glucan-based biological response modifiers are a potential strategy in lieu of their beneficial metabolic effects. Aureobasidium pullulans strains AFO-202 and N-163 beta-glucans were evaluated for anti-fibrotic and anti-inflammatory hepatoprotective potentials in a NASH animal model in this study. Methods In the STAM™ murine model of NASH, five groups were studied for 8 weeks: (1) vehicle (RO water), (2) AFO-202 beta-glucan; (3) N-163 beta-glucan, (4) AFO-202+N-163 beta-glucan, and (5) telmisartan (standard pharmacological intervention). Evaluation of biochemical parameters in plasma and hepatic histology including Sirius red staining and F4/80 immunostaining were performed. Results AFO-202 beta-glucan significantly decreased inflammation-associated hepatic cell ballooning and steatosis. N-163 beta-glucan decreased fibrosis and inflammation significantly ( P value < 0.05). The combination of AFO-202 with N-163 significantly decreased the NAFLD Activity Score (NAS) compared with other groups. Conclusion This preclinical study supports the potential of N-163 and AFO-202 beta-glucans alone or in combination as potential preventive and therapeutic agent(s), for NASH.
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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) | 0.000 | 0.000 |
| 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".