Evosep webinar

Tracking early signs of infectious disease with Evosep One to improve patient outcomes

Available on demand

In this session, we explore how cutting-edge technology can detect the early signs of infectious disease with precision and speed. Early identification of disease markers can significantly enhance patient care by allowing for faster, more effective treatments.

Learn how Evosep One’s robustness, sensitivity, and efficiency are helping laboratories and clinicians stay ahead of disease progression, providing key insights that lead to better health outcomes.

SPEAKERS

Antigen-derived peptides for high-resolution detection in the patients with mycobacterial infections

Talk by PhD. Tony Hu, Center for Cellular & Molecular Diagnostics, Tulane Cancer Center Translational Oncology Research Program Leader

Accurate and early detection of mycobacterial pathogens is critical for improving patient outcomes. We developed and validated multiple novel diagnostic methods. First, an immuno-affinity liquid chromatography–tandem mass spectrometry (ILM) assay that quantifies Mycobacterium tuberculosis CFP10 peptides and HIV-1 p24 in serum, showing high sensitivity and specificity in both adults and children. This method allows early detection of HIV and TB and monitors treatment responses effectively. The second approach involves an automated peptidomics pipeline that rapidly identifies mycobacterial species and subspecies from early growth cultures, including drug-resistant strains, significantly reducing diagnosis time compared to current clinical practices. These methods present significant advancements for managing the mycobacterial diseases.

Population scale proteomics enables adaptive digital twin modelling in sepsis

Talk by Aaron Scott, Infection Medicine (BMC) – Faculty of Medicine, Lund University.

Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.