The Microbiome and Alzheimer’s Disease: Potential and Limitations of Prebiotic, Synbiotic, and Probiotic Formulations
Bibliographic record
Abstract
The Microbiome has generated significant attention for its impacts not only on gastrointestinal health, but also on signaling pathways of the enteric and central nervous system via the microbiome gut-brain axis. In light of this, microbiome modulation may be an effective therapeutic strategy for treating or mitigating many somatic and neural pathologies, including neurodegenerative disorders. Alzheimer's disease (AD) is a chronic neurodegenerative disease that interferes with cerebral function by progressively impairing memory, thinking and learning through the continuous depletion of neurons. Although its etiopathogenesis remains uncertain, recent literature endorses the hypothesis that probiotic, prebiotic and synbiotic supplementation alters AD-like symptoms and improves many of its associated disease biomarkers. Alternatively, a dysfunctional microbiota impairs the gut epithelial barrier by inducing chronic gastric inflammation, culminating in neuroinflammation and accelerating AD progression. The findings in this review suggest that probiotics, prebiotics or synbiotics have potential as novel biological prophylactics in treatment of AD, due to their anti-inflammatory and antioxidant properties, their ability to improve cognition and metabolic activity, as well as their capacity of producing essential metabolites for gut and brain barrier permeability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".