New therapeutic avenues in multiple sclerosis: Is there a place for gut microbiota-based treatments?
Bibliographic record
Abstract
The bidirectional interaction between the gut and the central nervous system (CNS), the so-called gut microbiota-brain axis, is reported to influence brain functions, thus having a potential impact on the development or the progression of several neurodegenerative disorders. Within this context, it has been documented that multiple sclerosis (MS), an autoimmune inflammatory, demyelinating, and neurodegenerative disease of the CNS, is associated with gastrointestinal symptoms, including constipation, dysphagia, and faecal incontinence. Moreover, some evidence suggests the existence of an altered gut microbiota (GM) composition in MS patients with respect to healthy individuals, as well as the potential influence of GM dysbiosis on typical MS features, including increased intestinal permeability, disruption of blood-brain barrier integrity, chronic inflammation, and altered T cells differentiation. Starting from these assumptions, the possible involvement of GM alteration in MS pathogenesis seems likely, and its restoration could represent a supplemental beneficial strategy against this disabling disease. In this regard, the present review will explore possible preventive approaches (including several dietary interventions, the administration of probiotics, prebiotics, synbiotics, and postbiotics, and the use of faecal microbiota transplantation) to be pursued as prophylaxis or in combination with pharmacological treatments with the aim of re-establishing a proper GM, thus helping to prevent the development of this disease or to manage it by alleviating symptoms or slowing down its progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".