Non-Invasive Assessment of Mild Stress-Induced Hyperthermia by Infrared Thermography in Laboratory Mice.

Stress-induced hyperthermia (SIH) is a physiological response to acute stressors in mammals, shown as an increase in core body temperature, with redirection of blood flow from the periphery to vital organs. Typical temperature assessment methods for rodents are invasive and can themselves elicit SIH, affecting the readout. Infrared thermography (IRT) is a promising non-invasive alternative, if shown to accurately identify and quantify SIH. We used in-house developed software ThermoLabAnimal 2.0 to automatically detect and segment different body regions, to assess mean body (Tbody) and mean tail (Ttail) surface temperatures by IRT, along with temperature (Tsc) assessed by reading of subcutaneously implanted PIT-tags, during handling-induced stress of pair-housed C57BL/6J and BALB/cByJ mice of both sexes (N = 68). SIH was assessed during 10 days of daily handling (DH) performed twice per day, weekly voluntary interaction tests (VIT) and an elevated plus maze (EPM) at the end. To assess the discrimination value of IRT, we compared SIH between tail-picked and tunnel-handled animals, and between mice receiving an anxiolytic drug or vehicle prior to the EPM. During a 30 to 60 second stress exposure, Tsc and Tbody increased significantly (p < 0.001), while Ttail (p < 0.01) decreased. We did not find handling-related differences. Within each cage, mice tested last consistently showed significantly higher (p < 0.001) Tsc and Tbody and lower (p < 0.001) Ttail than mice tested first, possibly due to higher anticipatory stress in the latter. Diazepam-treated mice showed lower Tbody and Tsc, consistent with reduced anxiety. In conclusion, our results suggest that IRT can identify and quantify stress in mice, either as a stand-alone parameter or complementary to other methods.

Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples.

Forecasting COVID-19 disease severity is key to supporting clinical decision making and assisting resource allocation, particularly in intensive care units (ICUs). Here, we investigated the utility of time- and frequency-related features of the backscattered signal of serum patient samples to predict COVID-19 disease severity immediately after diagnosis. ICU admission was the primary outcome used to define disease severity. We developed a stacking ensemble machine learning model including the backscattered signal features (optical fingerprint), patient comorbidities, and age (AUROC = 0.80), which significantly outperformed the predictive value of clinical and laboratory variables available at hospital admission (AUROC = 0.71). The information derived from patient optical fingerprints was not strongly correlated with any clinical/laboratory variable, suggesting that optical fingerprinting brings unique information for COVID-19 severity risk assessment. Optical fingerprinting is a label-free, real-time, and low-cost technology that can be easily integrated as a front-line tool to facilitate the triage and clinical management of COVID-19 patients.