<i>Lactobacillus</i> Combined with Inulin Ameliorated Memory Deficit by Modulating Gut Microbiota and Metabolic Profiles
Bibliographic record
Abstract
This study aimed to investigate the effects of a combination of Lactobacillus and inulin (LI) on memory impairment, gut microbiota composition, and metabolic profiles in APP/PS1 transgenic mice (MC). Mice treated with LI exhibited significant improvements in memory performance, along with marked reductions in Aβ plaque deposition, neuroinflammation, and oxidative stress levels. Treatment with LI significantly increased the relative abundances of unclassified_f__Lachnospiraceae, Faecalibaculum, and Blautia, while significantly reducing Candidatus_Saccharimonas. Metabolomics analysis revealed that LI treatment led to elevated levels of acetylcholine, LysoPC (17:0), and sphinganine 1-phosphate, and a significant decrease in phosphocholine level. KEGG pathway analysis indicated that glycerophospholipid and sphingolipid metabolism were likely involved. In particular, Faecalibaculum and Candidatus_Saccharimonas may improve memory by modulating glycerophospholipid and sphingolipid pathways, respectively. These findings offer novel insights into microbiota-targeted strategies for memory enhancement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".