Hyperthermia elevates brain temperature and improves behavioural signs in animal models of Autism spectrum disorder
Bibliographic record
Abstract
Abstract Autism Spectrum Disorders (ASD) are predominantly developmental in nature and largely genetically determined. There are some human data supporting the idea that fever can improve symptoms in some individuals but the human data for this are limited and there are almost no data to support this from animal models. In the current study we used a whole body hyperthermia (WBH) protocol and systemic inflammation induced by bacterial endotoxin (LPS) to dissociate temperature and inflammatory elements of fever in order to examine the impact of these environmental stressors on behavioural signs in two animal models relevant to ASD: C58BL/6 and Shank3B- mice. While only LPS induced inflammatory signatures in the brain, WBH and LPS induced both overlapping and distinct neuronal cFos activation in several brain regions and modest effects on heat shock gene expression. In behavioural experiments LPS significantly suppressed most activities over 24-48 hours while WBH reduced repetitive behaviours and improved social interaction in C58BL/6 mice. In Shank3B- mice WBH significantly reduced compulsive grooming. The data are the first, to our knowledge, to demonstrate that elevated body temperature, in the absence of underpinning inflammation, can improve some behavioural signs in two distinct animal models of ASD. Given the developmental and genetic nature of ASD, evidence that symptoms may be ameliorated by environmental perturbations indicates that there are possibilities for improving function in these individuals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".