Repeated cold exposures protect a mouse model of Alzheimer's disease against cold-induced tau phosphorylation
Bibliographic record
Abstract
OBJECTIVE: Old age is associated with a rise in the incidence of Alzheimer's disease (AD) but also with thermoregulatory deficits. Indicative of a link between the two, hypothermia induces tau hyperphosphorylation. The 3xTg-AD mouse model not only develops tau and amyloid pathologies in the brain but also metabolic and thermoregulatory deficits. Brown adipose tissue (BAT) is the main thermogenic driver in mammals, and its stimulation counteracts metabolic deficits in rodents and humans. We thus investigated whether BAT stimulation impedes AD neuropathology. METHODS: 15-month-old 3xTg-AD mice were subjected to repeated short cold exposures (RSCE), consisting of 4-hour sessions of cold exposure (4 °C), five times per week for four weeks, compared to animals kept at housing temperature. RESULTS: First, we confirmed that 3xTg-AD RSCE-trained mice exhibited BAT thermogenesis and improved glucose tolerance. RSCE-trained mice were completely resistant to tau hyperphosphorylation in the hippocampus induced by a 24-hour cold challenge. Finally, RSCE increased plasma levels of fibroblast growth factor 21 (FGF21), a batokine, which inversely correlated with hippocampal tau phosphorylation. CONCLUSIONS: Overall, BAT stimulation through RSCE improved metabolic deficits and completely blocked cold-induced tau hyperphosphorylation in the 3xTg-AD mouse model of AD neuropathology. These results suggest that improving thermogenesis could exert a therapeutic effect in AD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".