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Record W4407866776 · doi:10.1080/20473869.2025.2462915

The gut–brain axis in autism spectrum disorder: microbiome dysbiosis, probiotics, and potential mechanisms of action

2025· article· en· W4407866776 on OpenAlexaff
Ruchı Tıwarı, Jaya Raju Nandikola, Mukesh Kumar Dharmalingam Jothinathan, Kareemulla Shaik, G. Hemalatha, Dharani Prasad P, Vasanth Kumar Mohan

Bibliographic record

VenueInternational Journal of Developmental Disabilities · 2025
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsAutismDysbiosisGut–brain axisAutism spectrum disorderGut microbiomeMicrobiomePsychologyAction (physics)Gut floraNeuroscienceDevelopmental psychologyBioinformaticsMedicineImmunologyBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.291
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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