|Ph.D Student||Vishinkin Rotem|
|Subject||Detection of Tuberculosis from Skin|
|Department||Department of Chemical Engineering||Supervisor||PROF. Hossam Haick|
|Full Thesis text|
Background: Tuberculosis (TB) is an infectious disease with no specific symptoms, thus leading to delayed diagnosis and further spread. In 2017, there were 10 million new TB cases, and 1.6 million deaths. About 95% of TB cases occur in developing countries. TB is a leading killer of HIV-positive people, 0.3 million deaths among people with HIV, during 2017. Currently available diagnostic methods, based on sputum samples, have limitations in developing countries; therefore, a new diagnostic tool for TB diagnosis is required. A promising approach for TB detection relies on volatile organic compounds (VOCs) detection. Such approach, for the diagnosis of TB patients, is based on the detection of VOCs emitted from TB cells and detected directly from the skin headspace.
Objectives: The aim of my thesis was to develop of a new approach for the diagnosis of TB in high-risk individuals and monitoring of the progress of TB by TB volatolomics via skin headspace. More specifically my research goals were : 1) Examination of unique pattern of VOCs from the skin headspace samples of healthy, symptomatic non-TB and active pulmonary TB individuals in different geographical locations; 2) Development of nanomaterial-based sensors for detection of unique pattern of TB VOCs from the human skin; and 3) Use the developed sensors to distinguish between active TB, latent TB and healthy population.
Methods: Exploratory studies were conducted in South Africa and India. The skin was sampled at different body locations by several absorbing materials which were analyzed by both Gas Chromatography-Mass Spectrometry (GC-/MS) and nano-sensor array coupled with pattern recognition algorithms.
Results: During this research, new sampling and analysis methods were developed and resulted in a global classifier based on a sensor array coupled with quadratic discriminant function analysis (DFA) model that discriminated between confirmed active pulmonary TB status and non-TB and healthy statuses with 92.3% sensitivity, 80.4% specificity and 84.9% accuracy. Similar results were obtained by artificial neuron network (ANN) algorithms, indicating on the great potential of such tools. Confounding factors, such as HIV status, smoking and others were examined and their impact was eliminated. Further analysis presented the ability of the sensor array to discriminate between active and latent TB statuses. GC-MS analysis resulted in a unique pattern of VOCs which differ between active and non-TB participants in both geographical locations. Toluene was found to be with higher abundance among active TB patients, in both locations.
Conclusions: This research presents the first evidence for emission of active pulmonary TB-related VOCs from the skin. Non-invasive, sputum-free detection of the disease from the skin with nanomaterial-based sensor array in combination with pattern recognition pattern was shown and the obtained results meet the world health organization (WHO) requirements for a TB triage test. A diagnostic tool based on the results from this research are the fundamentals for developing a wearable patch to address the TB epidemic risk in both developing and developed countries.