A Comprehensive Review on the Role of the Gut Microbiome in Human Neurological Disorders
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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