Gut microbes in neurocognitive and mental health disorders
Bibliographic record
Abstract
Introduction As individuals age, the prevalence of neurocognitive and mental health disorders increases. Current biomedical treatments do not completely address the management of these conditions. Despite new pharmacological therapy the challenges of managing these diseases remain.There is increasing evidence that the Gut Microbiome (GM) and microbial dysbiosis contribute to some of the more prevalent mental health and neurocognitive disorders, such as depression, anxiety, obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), schizophrenia, bipolar disorder (BP), and dementia as well as the behavioural and psychological symptoms of dementia (BPSD) through the microbiota-gut-brain axis.Methodology: Scoping review about the effect of gut microbiota on neurocognitive and mental health disorders.Results This scoping review found there is an evolving evidence of the involvement of the gut microbiota in the pathophysiology of neurocognitive and mental health disorders. This manuscript also discusses how the psychotropics used to treat these conditions may have an antimicrobial effect on GM, and the potential for new strategies of management with probiotics and faecal transplantation.Conclusions This understanding can open up the need for a gut related approach in these disorders as well as unlock the door for the role of gut related microbiota management.KEY MESSAGESChallenges of managing mental health conditions remain in spite of new pharmacological therapy.Gut dysbiosis is seen in various mental health conditions.Various psychotropic medications can have an influence on the gut microbiota by their antimicrobial effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".