A biocompatible microdevice for core body temperature monitoring in the early diagnosis of infectious disease

Abstract

The early diagnosis of microbial infection is critical to the clinical instigation of effective post-exposure prophylaxis or therapy. However, diagnosis of infection is often attempted only when there are overt clinical signs, and for some of the serious human pathogens, this may jeopardize the efficacy of therapy. We have used a miniaturised sealed, implantable transponder incorporating a calibrated temperature sensor with an external receiver system, to monitor core body temperature (Tc) remotely. We have observed early changes in the diurnal rhythm of Tc, after infection of mice with bacterial pathogens. Changes in Tc preceded overt clinical signs by 3-10 h following challenge with Yersinia pestis, which causes acute infection, In contrast, changes in Tc were detected 11 days before clinical signs in mice exposed to Burkholderia pseudomallei, which causes a chronic syndrome. Significantly, mice pre-vaccinated against Y.pestis infection showed only slight and transient disruption to the diurnal rhythm for Tc, in the absence of clinical signs, when challenged with 10(6) median lethal doses of Y.pestis. This remote monitoring technology could be used to monitor changes in more than one physiological parameter and extrapolation of these data to the clinic would define the available therapeutic window in which diagnosis and post-exposure prophylaxis could be instigated, after a suspected exposure.

Establishment of lethal inhalational infection with Francisella tularensis (tularaemia) in the common marmoset (Callithrix jacchus)

“…A deviation from this diurnal Tc pattern was the first indication of infection with F. tularensis, with the appearance of overt clinical signs, particularly piloerection, at 12-18 h later. Temperature increases followed by clinical signs have been reported previously in murine studies (Williamson et al 2007). In the present study, a longer delay between temperature increase and the onset of clinical signs was observed.…”

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

“…Previous work on physiological signal-based early infection detection work has been heavily focused on systemic bacterial infection ( Korach et al, 2001 ; Chen and Kuo, 2007 ; Ahmad et al, 2009 ; Papaioannou et al, 2012 ; Scheff et al, 2012 , 2013b ), and largely centered upon higher sampling rates of body core temperature ( Williamson et al, 2007 ; Papaioannou et al, 2012 ), advanced analyses of strongly-confounded signals such as heart rate variability ( Korach et al, 2001 ; Chen and Kuo, 2007 ; Ahmad et al, 2009 ) or social dynamics ( Madan et al, 2010 ), or sensor data fusion from already symptomatic (febrile) individuals ( Sun et al, 2013 ). While great progress has been made in developing techniques for physiological-signal based early warning of bacterial infections and other critical illnesses in a hospital setting ( Heldt et al, 2006 ; Liu et al, 2011 , 2014a ; Lehman et al, 2014 ), efforts to extend these techniques to viral infections or other communicable pathogens in non-clinical contexts using wearable sensor systems have only recently been pursued in observational studies on human subjects, primarily as a rapid response to the COVID-19 pandemic ( Li et al, 2017 ; Miller et al, 2020 ; Mishra et al, 2020 ; Natarajan et al, 2020 ; Quer et al, 2021 ).…”

Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

“…Furthermore, molecular diagnostics are rarely used until patient self-reporting and presentation of overt clinical symptoms, such as fever. Past physiological signal-based early infection detection work has been heavily focused on systemic bacterial infection [30][31][32][33][34][35], and largely centered upon higher sampling rates of body core temperature [35,36], advanced analyses of stronglyconfounded signals such as heart rate variability [31][32][33] or social dynamics [37], or sensor data fusion from already symptomatic (febrile) individuals [38]. While great progress has been made in developing techniques for signal-based early warning of bacterial infections and other critical illnesses in a hospital setting [39][40][41][42], we are aware of only one prior effort to extend these techniques to viral infections or other communicable pathogens in non-clinical contexts using wearable sensor systems [43].…”