The gut–brain axis in autism spectrum disorder: microbiome dysbiosis, probiotics, and potential mechanisms of action
Bibliographic record
Abstract
The gut–brain axis has garnered significant attention in autism spectrum disorder (ASD) research, particularly focusing on microbiome dysbiosis and probiotics as potential interventions. This abstract explores the interplay between and the mechanisms of action. Individuals with ASD commonly exhibit gut microbiome alterations, implicating dysbiosis in ASD pathogenesis. Probiotics, which are beneficial bacterial supplements, have emerged as a promising therapeutic avenue because of their ability to modulate gut microbiota. Potential mechanisms underlying their efficacy include restoration of microbial balance, regulation of immune responses, and production of neuroactive compounds. Probiotics may mitigate gastrointestinal symptoms and ameliorate behavioural manifestations in ASD by influencing gut microbiota composition. Understanding the intricate connections between the gut and the brain in ASD, along with the therapeutic potential of probiotics, offers promising avenues for intervention. Further research elucidating the specific mechanisms and optimizing probiotic formulations could lead to more effective treatments for individuals with ASD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".