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Continuous and non-invasive thermography of mouse skin accurately describes core body temperature patterns, but not absolute core temperature.

Vincent van der VinneCarina A PothecarySian L WilcoxLaura E McKillopLindsay A BensonJenya KolpakovaShu K E TamLukas B KroneAngus S FiskTatiana S WilsonTomoko YamagataJames CantleyVladyslav V VyazovskiyStuart N Peirson
Published in: Scientific reports (2020)
Body temperature is an important physiological parameter in many studies of laboratory mice. Continuous assessment of body temperature has traditionally required surgical implantation of a telemeter, but this invasive procedure adversely impacts animal welfare. Near-infrared thermography provides a non-invasive alternative by continuously measuring the highest temperature on the outside of the body (Tskin), but the reliability of these recordings as a proxy for continuous core body temperature (Tcore) measurements has not been assessed. Here, Tcore (30 s resolution) and Tskin (1 s resolution) were continuously measured for three days in mice exposed to ad libitum and restricted feeding conditions. We subsequently developed an algorithm that optimised the reliability of a Tskin-derived estimate of Tcore. This identified the average of the maximum Tskin per minute over a 30-min interval as the optimal way to estimate Tcore. Subsequent validation analyses did however demonstrate that this Tskin-derived proxy did not provide a reliable estimate of the absolute Tcore due to the high between-animal variability in the relationship between Tskin and Tcore. Conversely, validation showed that Tskin-derived estimates of Tcore reliably describe temporal patterns in physiologically-relevant Tcore changes and provide an excellent measure to perform within-animal comparisons of relative changes in Tcore.
Keyphrases
  • machine learning
  • minimally invasive
  • metabolic syndrome
  • deep learning
  • neural network