The Effects of Prebiotics and Probiotics Following Antibiotic Exposure in a Mouse Model of Autism Spectrum Disorder: A Research Protocol
Bibliographic record
Abstract
The human microbiota consists of 10-100 trillion symbiotic microbial cells critical for one’s digestive system, immune system and for managing neurological symptoms experienced in neurodevelopmental conditions. With early exposure to antibiotics, an individual’s microbiome composition is negatively affected by reducing the diversity of microbial species found in the microbiome and can lead to an imbalance in the Gut-Brain-Axis (GBA). While the direct relationship between the GBA and neurodevelopmental functioning is still unclear, evidence suggests that individuals with a disruptive microbiome and an imbalanced GBA have an increased risk of developing neurodevelopmental disorders, specifically autism spectrum disorder (ASD). To improve microbiome diversity, exposure to a high prebiotic and probiotic diet in the early stages of life can reintroduce beneficial bacteria back into the microbiome and improve microbial diversity. Pre- and pro-biotics can improve microbiome diversity and restore balance to the GBA. With the introduction of a prebiotic and probiotic diet and a balanced GBA, there is a possibility to reduce the severity of ASD symptoms. By reducing the severity of ASD symptoms, the quality of life of those with severe ASD can potentially be improved allowing them to maintain functional independence. This research protocol intends to utilize an automated video tracking system and three-chambered social approach to evaluate the behavioural symptoms in BTBR strain mice which exhibit symptoms before and after administration of pre- and probiotics following antibiotic exposure.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".