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New breathalyser to diagnose diseases with just one puff

JERUSALEM, Jan 1:  Doctors may soon determine a person’s risk of 17 different and unrelated diseases – including Parkinson’s and various cancers – just by taking a quick read of their breath, thanks to a new low-cost and non-invasive device developed by researchers in Israel.

Diagnostic techniques based on breath samples have been demonstrated in the past, but until now, there has not been scientific proof of the hypothesis that different and unrelated diseases are characterised by distinct chemical breath signatures.

Technologies developed to date for this type of diagnosis have been limited to detecting a small number of clinical disorders, without differentiation between unrelated diseases.

The study of more than 1,400 patients included 17 different and unrelated diseases: lung cancer, colorectal 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 disease (two types), multiple sclerosis, pulmonary hypertension, preeclampsia and chronic kidney disease.

Samples were collected between January 2011 and June 2014 from  14 departments at nine medical centres in five countries: Israel, France, the US, Latvia and China.

Researchers led by Professor Hossam Haick from Technion-Israel Institute of Technology in Israel tested the chemical composition of the breath samples using an accepted analytical method (mass spectrometry), which enabled accurate quantitative detection of the chemical compounds they contained.

As many as 13 chemical components were identified, in different compositions, in all 17 of the diseases.

“Each of these diseases is characterised by a unique fingerprint, meaning a different composition of these 13 chemical components,” said Haick.

“Just as each of us has a unique fingerprint that distinguishes us from others, each disease has a chemical signature that distinguishes it from other diseases and from a normal state of health. These odour signatures are what enables us to identify the diseases using the technology that we developed,” Haick said.

With a new technology called “artificially intelligent nanoarray,” developed by Haick, the researchers were able to corroborate the clinical efficacy of the diagnostic technology.

The array enables fast and inexpensive diagnosis and classification of diseases, based on “smelling” the patient’s breath and using artificial intelligence to analyse the data obtained from the sensors.

Some of the sensors are based on layers of gold nanoscale

particles and others contain a random network of carbon nanotubes coated with an organic layer for sensing and identification purposes.

The study also assessed the efficiency of the artificially intelligent nanoarray in detecting and classifying various diseases using breath signatures.

To verify the reliability of the system, the team also examined the effect of various factors (such as gender, age, smoking habits and geographic location) on the sample composition, and found their effect to be negligible and without impairment on the array’s sensitivity.

“Each of the sensors responds to a wide range of exhalation components and integration of the information provides detailed data about the unique breath signatures characteristic of the various diseases,” said Haick and his previous PhD student, Morad Nakhleh.

“Our system has detected and classified various diseases with an average accuracy of 86 per cent,” they said.

“This is a new and promising direction for diagnosis and classification of diseases, which is characterised not only by considerable accuracy but also by low cost, low electricity consumption, miniaturisation, comfort and the possibility of repeating the test easily,” they added.

“Breath is an excellent raw material for diagnosis. It is available without the need for invasive and unpleasant procedures, it’s not dangerous, and you can sample it again and again if necessary,” Haick added.

The study was published in the journal ACS Nano. (PTI)


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