Department of Neurology and Department of Experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
QUEST - Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Berlin, Germany.
Sci Rep. 2018 Feb 23;8(1):3526. doi: 10.1038/s41598-018-22020-6.
Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters.
体温是判断实验动物健康状况的一个重要参数。然而,在急性疾病期间,利用体温来细化人道终点通常不够全面,并容易受到观察者之间的偏差影响。在这里,我们比较了两种评估小鼠体温的方法,即植入式射频识别 (RFID) 温度传感器(方法 1)和非接触式红外测温仪(方法 2),在正常体温和脂多糖 (LPS) 内毒素诱导的低体温期间,对 435 只小鼠进行了长达 7 天的测量。方法 1 和 2 分别确定的核心温度和表面温度之间具有极好的一致性,而方法 2 的个体内和个体间变异性更高。然而,使用机器学习算法来确定基于温度的终点,两种方法在预测死亡作为结果事件方面都具有出色的准确性。因此,非接触式红外测温仪虽然需要在实验者之间进行标准化,但作为基于植入式传感器系统的低温反应的可靠替代方法,价格更低廉且操作更简便。