Invited Review: From nose to gut – the role of the microbiome in neurological disease
Bibliographic record
Abstract
Inflammation and neurodegeneration are key features of many chronic neurological diseases, yet the causative mechanisms underlying these processes are poorly understood. There has been mounting interest in the role of the human microbiome in modulating the inflammatory milieu of the central nervous system (CNS) in health and disease. To date, most research has focussed on a gut-brain axis, with other mucosal surfaces being relatively neglected. We herein take the novel approach of comprehensively reviewing the roles of the microbiome across several key mucosal interfaces - the nose, mouth, lung and gut - in health and in Parkinson's disease (PD), Alzheimer's disease (AD) and multiple sclerosis (MS). This review systematically appraises the anatomical and microbiological landscape of each mucosal surface in health and disease before considering relevant mechanisms that may influence the initiation and progression of PD, AD and MS. The cumulative effects of dysbiosis from the nose to the gut may contribute significantly to neurological disease through a wide variety of mechanisms, including direct translocation of bacteria and their products, and modulation of systemic or CNS-specific immunity. This remains an understudied and exciting area for future research and may lead to the development of therapeutic targets for chronic neurological disease.
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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 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".