Thermoneutrality and Immunity: How Does Cold Stress Affect Disease?
Bibliographic record
Abstract
One of the major challenges the scientific community faces today is the lack of translational data generated from mouse trials for human health application. Housing temperature-dependent chronic cold stress in laboratory rodents is one of the key factors contributing to lack of translatability because it reveals major metabolic differences between humans and rodents. While humans tend to operate at temperatures within their thermoneutral zone, most laboratory rodents are housed at temperatures below this zone and have an increased energy demand to generate heat. This has an impact on the immune system of mice and thus affects results obtained using murine models of human diseases. A limited number of studies and reviews have shown that results obtained on mice housed at thermoneutrality were different from those obtained from mice housed in traditional housing conditions. Most of those studies, focused on obesity and cancer, found that housing mice at thermoneutrality changed the outcomes of the diseases negatively and positively, respectively. In this review, we describe how thermoneutrality impacts the immune system of rodents generally and in the context of different disease models. We show that thermoneutrality exacerbates cardiovascular and auto-immune diseases; alleviates asthma and Alzheimer's disease; and, changes gut microbiome populations. We also show that thermoneutrality can have exacerbating or alleviating effects on the outcome of infectious diseases. Thus, we join the call of others in this field to urge researchers to refine murine models of disease and increase their translational capacity by considering housing at thermoneutrality for trials involving rodents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".