Extracts of Salvia‐Nelumbinis Naturalis Ameliorate Nonalcoholic Steatohepatitis via Inhibiting Gut‐Derived Endotoxin Mediated TLR4/NF‐<i>κ</i>B Activation
Bibliographic record
Abstract
Nonalcoholic steatohepatitis (NASH) is featured by the presence of hepatic steatosis combined with inflammation and hepatocellular injury. Gut‐derived endotoxin plays a crucial role in the pathogenesis of NASH. Salvia‐Nelumbinis naturalis (SNN), a formula of Traditional Chinese Medicine, has been identified to be effective for NASH, but the mechanisms were not thoroughly explored. In the present study, a NASH model was generated using C57BL/6 mice fed a high fat diet (HFD) supplemented periodically with dextran sulfate sodium (DSS) in drinking water for 12 weeks. Mice fed HFD alone (without DSS) or chow diet were used as controls. The NASH mice were given the SNN extracts in the following 4 weeks, while control mice were provided with saline. Mice fed HFD developed steatosis, and DSS supplementation resulted in NASH. The SNN extracts significantly improved metabolic disorders including obesity, dyslipidemia, and liver steatosis and reduced hepatic inflammation, circulating tumor necrosis factor‐α (TNF‐α), and lipopolysaccharide (LPS) levels. The beneficial effect of the SNN extracts was associated with restoration of intestinal conditions (microbiota, integrity of intestinal barrier) and inhibition of TLR4/NF‐κB activation. These results suggest that the SNN extracts ameliorate NASH progression, possibly through blocking endotoxin related TLR4/NF‐κB activation.
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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) | 0.000 | 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 | 0.000 |
| 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".