Different diseases are characterized by different "chemical signatures" that can be identified based on a breath sample

Prof. Hussam Haik of the Technion's Faculty of Chemical Engineering, who developed an innovative technology for medical diagnosis based on the oral cavity, led an unprecedented international study in which 1,404 subjects participated. The system developed by Prof. Haik's research team was able to diagnose diseases with an average accuracy of 86% 

Prof. Hussam Haik from the Faculty of Chemical Engineering at the Technion, who developed an innovative technology for medical diagnosis based on the oral cavity, publishes in the scientific journal ACS Nano the first international study of its scope on this topic. The study, which was conducted as part of Dr. Murad Nakhla's doctoral thesis in collaboration with 56 other researchers, took place in five countries with the participation of 1,404 subjects and examined 17 different diseases that are not dependent on each other.

In the aforementioned article, the Technion researchers provide the first scientific proof of the 2,400-year-old hypothesis, according to which breath tests may provide important information about clinical conditions, and verify the clinical effectiveness of the diagnostic technology developed by the group. This technology enables the diagnosis and sorting of diseases in a quick and cheap way, based on the "smelling" of the subject's oral cavity through an advanced sensing system and artificial intelligence.

"Identifying physiological disorders based on substances emitted from the body is an age-old approach that was recorded as early as the time of ancient Greece," explains Prof. Haik, "and oral swabs are excellent raw material for diagnosis. This is because it is available without the need for invasive processes and unpleasant operations, it is not dangerous and it can be sampled again and again if necessary. In the past, diagnostic methods based on breath samples have already been demonstrated, but so far no scientific proof has been provided for the hypothesis that different and independent diseases are characterized by different chemical signatures of the oral cavity. In addition, the technologies developed so far for this type of diagnosis are limited to detecting a limited number of clinical disorders and do not distinguish between different and independent types of diseases."

As mentioned, 17 different diseases were included in the current study: lung cancer, bowel cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, stomach cancer, Crohn's disease, ulcerative colitis, irritable bowel syndrome, Parkinson's (of two types), Multiple sclerosis, pulmonary hypertension, preeclampsia and chronic kidney disease. The samples were collected between January 2011 and June 2014 from 1,404 subjects in 14 departments in 9 medical centers in 5 countries: Israel, France, USA, Latvia and China. This is of course according to the protocol of the Helsinki Committee and with the approval of the participants in the study.

At the same time as the samples are monitored with the new technology, their chemical composition is tested using an accepted analytical method (mass spectrometry) which allows accurate quantitative identification of chemical compounds present in the breath samples. Thus, 13 chemical components were identified which are found, in different compositions, in all 17 diseases. According to Prof. Haik and Dr. Nachla, "Each of the aforementioned diseases is characterized by a unique fingerprint, that is, by a different composition of the aforementioned 13 chemical components. Just as each of us has a unique fingerprint that distinguishes us from others, so each disease has a chemical signature that distinguishes it from other diseases and from a normal state of health. These odor stamps are what allow us to identify the diseases using the technology we have developed."

At the same time as the scientific work, the research examined the effectiveness of the new assessment, called an "artificially intelligent nanoarray" in the detection and sorting of the various diseases using oral cavity seals. It is a multi-sensor system that analyzes the information received from the sensors using artificial intelligence. Some sensors are based on layers of nanoscale gold particles and others contain a random network of carbon nanotubes coated with an organic layer for sensing and detection. To verify the reliability of the system, the effects of various factors (such as gender, age, smoking habits and geographic location) on the composition of the sample were tested, and it was found that this is a negligible effect that does not harm the sensitivity of the nanometer array.

"Each of the sensors responds to a wide variety of components in the breath," explains Prof. Haik, "and the integration of the information provides detailed data on the unique signatures of the breath characteristic of various diseases. Our system detected and sorted the various diseases with an average accuracy of 86%. Therefore, this is a new and promising direction for the diagnosis and sorting of diseases, characterized not only by high accuracy but also by low cost, low power consumption, miniaturization, convenience and the possibility of easily repeating the test."

Prof. Haik, a full professor at the Technion who heads three major European conglomerates, has won many prestigious grants and awards, including the Marie Curie Excellence Grant, a European Research Council grant, grants from the Bill and Melinda Gates Foundation, the Herschel Ritz Prize (Technion) and the Humboldt Prize (Germany) ). He is included in several important lists, including the list of the top 35 young scientists in the world (ranked by MIT), the list of 100 by Nominate Trust (London), which includes the 100 most influential digital inventors and developers in the world, and the list of the XNUMX most influential people in the world on behalf of GOOD magazine published in Los Angeles Les. Prof. Haik also won the highest teaching award given by the Technion - the Yanai Award for Excellence in Academic Education.

For the full article in the scientific journal ACS Nano  click here