Alistipes: The influence of a commensal on anxiety and depression
Bibliographic record
Abstract
The interaction between the gut microbiome and the brain is increasingly recognized as a potential cause for pathophysiology. With a variety of mechanisms for altering host central nervous system (CNS) function, including tryptophan metabolism and releasing modulatory metabolites, the human microbiome is emerging as a target for the development of therapies against disorders such as anxiety and depression. In this review, the gut microbiota and the microbiome-gut-brain axis will be discussed. Then, the mechanisms by which gut microbiota interacts with the CNS with a focus on anxiety and depression will be outlined. Following this, potential mechanisms whereby Alistipes may modulate behaviour including the inflammatory, serotonin and secondary metabolites hypotheses are highlighted. Throughout the review controversies involving these pathways are mentioned. Elucidating a mechanism for a clear link between Alistipes and anxiety/depression may lead to novel approaches to treating these disorders.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Review of gut microbiota mechanisms in anxiety and depression; domain biology.
The review concerns gut microbiome mechanisms in anxiety and depression.
Review of gut microbiota species and anxiety/depression; biomedical object.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".