A Comprehensive Review on the Role of the Gut Microbiome in Human Neurological Disorders
Bibliographic record
Abstract
The human body is full of an extensive number of commensal microbes, consisting of bacteria, viruses, and fungi, collectively termed the human microbiome. The initial acquisition of microbiota occurs from both the external and maternal environments, and the vast majority of them colonize the gastrointestinal tract (GIT). These microbial communities play a central role in the maturation and development of the immune system, the central nervous system, and the GIT system and are also responsible for essential metabolic pathways. Various factors, including host genetic predisposition, environmental factors, lifestyle, diet, antibiotic or nonantibiotic drug use, etc., affect the composition of the gut microbiota. Recent publications have highlighted that an imbalance in the gut microflora, known as dysbiosis, is associated with the onset and progression of neurological disorders. Moreover, characterization of the microbiome-host cross talk pathways provides insight into novel therapeutic strategies. Novel preclinical and clinical research on interventions related to the gut microbiome for treating neurological conditions, including autism spectrum disorders, Parkinson's disease, schizophrenia, multiple sclerosis, Alzheimer's disease, epilepsy, and stroke, hold significant promise. This review aims to present a comprehensive overview of the potential involvement of the human gut microbiome in the pathogenesis of neurological disorders, with a particular emphasis on the potential of microbe-based therapies and/or diagnostic microbial biomarkers. This review also discusses the potential health benefits of the administration of probiotics, prebiotics, postbiotics, and synbiotics and fecal microbiota transplantation in neurological disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".