Invited Review: From nose to gut – the role of the microbiome in neurological disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it