Microbiome Engineering for Biotherapeutic in Alzheimer’s Disease Through the Gut–Brain Axis: Potentials and Limitations
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition characterized by considerable cognitive decline and functional impairment, primarily due to the progressive alteration of neurons, microglia, and astrocytes. Pathological manifestations of AD include the loss of synaptic plasticity, reduction in synaptic strength by amyloid-beta, aggregation, and neurotoxicity from tau protein post-translational modifications, all contributing to the disruption of neural networks. Despite its current pharmacological treatment for AD, different approaches to treat such disease are being developed, from a microbiome perspective. The microbiome encompasses a diverse microorganism, including beneficial bacteria that create a positive impact to diminish AD pathogenesis. Growing evidence suggests that probiotic, prebiotic, synbiotic, and postbiotics can positively modulate the gut-brain axis, reducing systemic inflammation, restoring neurotransmitter balance, and improving gut health, thereby possibly mitigating AD pathogenesis. Moreover, there is paraprobiotics as the most recently developed biotherapeutic with beneficial effects. This review explores the correlation between AD and gut-brain axis as a novel biotherapeutic target. The underlying mechanism of the microbiota-gut-brain axis in AD is examined. Novel insights into the current applications as potential treatment and its limitations are highlighted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".