Investigation of the Mitigating Mechanism of <i>Bifidobacterium longum</i> 300 in <scp>d</scp>-Galactose-Induced Cognitive Impairment in Mice
Bibliographic record
Abstract
People’s aging is accompanied by cognitive impairment, especially the decline in learning and memory ability. Probiotics have potential health-promoting functions in this regard. In this study, the function of Bifidobacterium longum 300 (BL300) in reducing cognitive impairment of mice induced via d -galactose was evaluated, and the mitigating mechanism of this function was deeply researched. Treatment with BL300 significantly enhanced the nesting ability of the mice, increased their time spent in the target quadrant of the water maze, and reduced the number of errors in passive avoidance tests (PAT). Notably, BL300 treatment facilitated recovery in the gut microbiota composition by increasing beneficial microbial populations (e.g., Lactobacillus ) and decreasing pathogenic bacteria. Importantly, compared to mice treated with d -galactose, the indices of inflammation and acetylcholinesterase levels in the hippocampus and serum were decreased, along with a downregulation of pro-inflammatory cytokines in the brain (such as TNF-α and IL-6) when they were fed with BL300. Furthermore, the administration of BL300 resulted in an increase in the populations of BDNF + and NeuN + cells in the hippocampus and reduced intestinal permeability. Therefore, this study provides compelling evidence for the BL300 can reduce cognitive decline, and BL300 would be regarded as a potential probiotic for alleviating cognitive impairment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".