Application of a thermoelectric cooling approach for localized hypothermia in a murine model
Bibliographic record
Abstract
Achieving localized and adjustable hypothermia is critical for various clinical and experimental applications, including reducing oxidative stress, modulating inflammatory responses, and enabling temperature-triggered drug delivery. However, existing cooling techniques such as ice packs, and cryogenic sprays limitations in precision, efficiency, duration, and cooling capacity. In this study, we used a commercially available thermoelectric cooling module to construct a simple and low-cost cooling system, and applied it in a preclinical mouse model to achieve focal hypothermia at a subcutaneous transplant site.•The system, assembled using a TES1-4903 thermoelectric module, a heat sink, and a power supply, achieved rapid temperature reduction rates. At 5 V, the subcutaneous temperature decreased at an average rate of ∼1.5 °C/s during the first 10 s, reaching a stable temperature of ∼8 °C within 120 s. At 2 V, the average rate was ∼0.4 °C/s, stabilizing at ∼17 °C over the same period.•The system demonstrated precise temperature control with minimal variability, maintaining temperature steps of <2 °C and ensuring a stable temperature range.•Compared to literature, our system highlights the utility of thermoelectric modules for biomedical cooling applications, demonstrating faster and safer subcutaneous hypothermia with more precise temperature control than other approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".