Diet alters the effects of lipopolysaccharide on intestinal health and cecal microbiota composition in C57Bl/6 male mice
Bibliographic record
Abstract
Lipopolysaccharide (LPS), a component of the gram-negative bacteria, induces an inflammatory cascade in mice, negatively impacting aspects of the microbiota gut-brain axis (mGBA). Flaxseed (FS), an oilseed enriched in dietary fiber and n3 poly-unsaturated fatty acids, has been shown to partially attenuate LPS-induced systemic and neuroinflammation. In this study, we investigated the impact of FS and FS oil (FO) diets on microbial dysbiosis, biomarkers of intestinal health, hepatic inflammation and oxidative stress, and metabolic homeostasis in male mice, 24-hours post LPS-exposure. Compared to saline-treated mice, LPS mice showed diet-dependent shifts in the cecal microbiome. Most notably, LPS-treated basal diet (BD)-fed mice had reduced Muribaculaceae and Lachnospiraceae, FS-LPS mice had elevated Akkermansia and Enterobacteriaceae, and both the FS-LPS and FO-LPS mice had increased Bacteroides. LPS increased cecal short-chain fatty acid concentrations, the highest of which were found in FS-fed mice. Intestinal health biomarkers were modulated by LPS in a diet-specific manner such that ileal mucous content was elevated in FS- and FO-fed mice, while LPS-induced inflammation (IL-1β) was attenuated in FS-fed mice. On the other hand, LPS-induced hepatic inflammation and oxidative stress, which were not attenuated by FS or FO diets. Bacteroides abundance and serum Il-10 levels, and cecal butyrate concentrations and hippocampal IL-6 mRNA, were negatively correlated in FS-fed mice only, suggesting a potential role of the microbiome in the anti-inflammatory effects of FS post-LPS treatment. Collectively, LPS exposure negatively impacted the cecal microbiome and markers of intestinal, hepatic, and metabolic health, the former being beneficially altered by FS diet.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".