Conference Agenda

Babesiosis, caused by intraerythrocytic parasites of the genus Babesia, is an emerging zoonotic threat with a global distribution. Babesiosis ranges from asymptomatic to fulminant disease occurring predominantly in immunocompromised hosts, with fatality rates up-to 20%. Diagnosis traditionally relies on microscopic examination of blood smears; however, up-to 20% of infections are sub‑microscopic, risking transmission via blood transfusion, organ transplantation, and the ixodid tick vector. While molecular and immunodiagnostic methods exhibit improved sensitivity, they are costly, time‑consuming, technically complex, and invasive. Near‑infrared spectroscopy (NIRS) utilises near-infrared electromagnetic energy (350–2500 nm) to generate spectral signatures reflective of specific chemical changes in a biological sample. When coupled with machine learning algorithms, diagnostic features can be extracted, allowing sample classification. NIRS has been successfully applied as a non-invasive malaria diagnostic in mice and humans; however analogous studies have not been performed with Babesia spp. This study evaluated the ability of NIRS to non-invasively detect B. microti in mice. The sensitivity and specificity of non-invasive detection was compared to invasive detection (blood spots). By situating NIRS alongside established molecular detection methods, our goal is to lay the groundwork for a rapid, reagent‑free diagnostic that complements existing assays while enabling non‑invasive babesiosis surveillance and enhanced donor screening.