The gut microbiome in human neurological disease: A review
Bibliographic record
Abstract
Almost half the cells and 1% of the unique genes found in our bodies are human, the rest are from microbes, predominantly bacteria, archaea, fungi, and viruses. These microorganisms collectively form the human microbiota, with most colonizing the gut. Recent technological advances, open access data libraries, and application of high-throughput sequencing have allowed these microbes to be identified and their contribution to neurological health to be examined. Emerging evidence links perturbations in the gut microbiota to neurological disease, including disease risk, activity, and progression. This review provides an overview of the recent advances in microbiome research in relation to neuro(auto)immune and neurodegenerative conditions affecting humans, such as multiple sclerosis, neuromyelitis optica spectrum disorders, Parkinson disease, Alzheimer disease, Huntington disease, and amyotrophic lateral sclerosis. Study design and terminology used in this rapidly evolving, highly multidisciplinary field are summarized to empower and engage the neurology community in this "newly discovered organ." Ann Neurol 2017;81:369-382.
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.
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) | 0.003 | 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".