Label-free and real-time detection of tuberculosis in human urine samples using a nanophotonic point-of-care platform

P. Ramirez-Priego, D. Martens, A. A. Elamin, P. Soetaert, W. Van Roy, R. Vos, B. Anton, R. Bockstaele, H. Becker, M. Singh, P. Bienstman, L. M. Lechuga

ACS Sensors, 2018 - DOI: 10.1021/acssensors.8b00393

Abstract: Tuberculosis (TB) is the leading global cause of death from a single infectious agent. Registered incidence
rates are low, especially in low-resource countries with weak health systems, due to the disadvantages of current diagnostic
techniques. A major effort is directed to develop a point-of-care (POC) platform to reduce TB deaths with a prompt
and reliable low-cost technique. In the frame of the European POCKET Project, a novel POC platform for the direct and
non-invasive detection of TB in human urine was developed. The photonic sensor is integrated in a disposable cartridge
and is based on a highly sensitive Mach-Zehnder Interferometer (MZI) transducer combined with an on-chip spectral
filter. The required elements for the read-out are integrated in an instrument prototype, which allows real-time monitoring
and data processing. In this work, the novel POC platform has been employed for the direct detection of lipoarabinomannan
(LAM), a lipopolysaccharide found in the mycobacterium cell wall. After the optimization of several parameters,
a limit of detection of 475 pg/mL (27.14 pM) was achieved using a direct immunoassay in undiluted human urine in
less than 15 minutes. A final validation of the technique was performed using twenty clinical samples from TB patients
and healthy donors, allowing the detection of TB in people regardless of HIV coinfection. The results show excellent correlation
to those obtained with standard techniques. These promising results demonstrate the high sensitivity, specificity
and applicability of our novel POC platform, which could be used during routine check-ups in developing countries.

Label-free and real-time detection of tuberculosis in human urine samples using a nanophotonic point-of-care platform
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