Food for thought: probiotic modulation of microglial activity in Parkinson's disease
Bibliographic record
Abstract
The gut-brain axis is emerging as a key player in Parkinson's disease (PD), with growing attention on how the gut microbiome (GM) shapes microglial activity, a central driver of neuroinflammation and dopaminergic loss. GM dysbiosis, characterized by reduced beneficial microbes and increased proinflammatory taxa, can compromise intestinal barrier integrity, activate systemic immunity, and prime microglia toward a proinflammatory state, potentially facilitating α-synuclein misfolding and propagation from gut to brain. Preclinical studies reveal that probiotics can rebalance microbial communities, enhance short-chain fatty acid production, reinforce intestinal barrier integrity, and modulate immune responses, effects collectively linked to reduced microglial reactivity, lower α-synuclein aggregation, and improved motor outcomes in PD models. Human trials of probiotic supplementation in PD, primarily investigating gastrointestinal and non-motor symptoms, suggest potential benefits for systemic inflammation and neuroimmune signaling, though direct evidence of central microglial modulation is limited. By synthesizing animal and clinical data, this review underscores both the therapeutic promise of probiotics and identifies current gaps in leveraging microbiota-based interventions as non-invasive, disease-modifying strategies for PD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".