Tag Archives: Hu Liu

Gold nanoparticles make a new promise: a non-invasive COVID-19 breathalyser

I believe that swab they stick up your nose to test for COVDI-19 is 10 inches long so it seems to me that discomfort or unpleasant are not the words that best describe the testing experience .

Hopefully, no one will have to find inadequate vocabulary for this new COVID-19 testing assuming that future trials are successful and they are able to put the technology into production. From an August 19, 2020 news item on Nanowerk,

Few people who have undergone nasopharyngeal swabs for coronavirus testing would describe it as a pleasant experience. The procedure involves sticking a long swab up the nose to collect a sample from the back of the nose and throat, which is then analyzed for SARS-CoV-2 RNA [ribonucleic acid] by the reverse-transcription polymerase chain reaction (RT-PCR).

Now, researchers reporting in [American Chemical Society] ACS Nano (“Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath”) have developed a prototype device that non-invasively detected COVID-19 in the exhaled breath of infected patients.

An August 19, 2020 ACS news release (also received via email and on EurekAlert), which originated the news item, provides more technical details,

In addition to being uncomfortable, the current gold standard for COVID-19 testing requires RT-PCR, a time-consuming laboratory procedure. Because of backlogs, obtaining a result can take several days. To reduce transmission and mortality rates, healthcare systems need quick, inexpensive and easy-to-use tests. Hossam Haick, Hu Liu, Yueyin Pan and colleagues wanted to develop a nanomaterial-based sensor that could detect COVID-19 in exhaled breath, similar to a breathalyzer test for alcohol intoxication. Previous studies have shown that viruses and the cells they infect emit volatile organic compounds (VOCs) that can be exhaled in the breath.

The researchers made an array of gold nanoparticles linked to molecules that are sensitive to various VOCs. When VOCs interact with the molecules on a nanoparticle, the electrical resistance changes. The researchers trained the sensor to detect COVID-19 by using machine learning to compare the pattern of electrical resistance signals obtained from the breath of 49 confirmed COVID-19 patients with those from 58 healthy controls and 33 non-COVID lung infection patients in Wuhan, China. Each study participant blew into the device for 2-3 seconds from a distance of 1¬-2 cm. Once machine learning identified a potential COVID-19 signature, the team tested the accuracy of the device on a subset of participants. In the test set, the device showed 76% accuracy in distinguishing COVID-19 cases from controls and 95% accuracy in discriminating COVID-19 cases from lung infections. The sensor could also distinguish, with 88% accuracy, between sick and recovered COVID-19 patients. Although the test needs to be validated in more patients, it could be useful for screening large populations to determine which individuals need further testing, the researchers say.

The authors acknowledge funding from the Technion-Israel Institute of Technology.

Here’s a link to and a citation for the paper,

Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath by Benjie Shan, Yoav Y Broza, Wenjuan Li, Yong Wang, Sihan Wu, Zhengzheng Liu, Jiong Wang, Shuyu Gui, Lin Wang, Zhihong Zhang, Wei Liu, Shoubing Zhou, Wei Jin, Qianyu Zhang, Dandan Hu, Lin Lin, Qiujun Zhang, Wenyu Li, Jinquan Wang, Hu Liu, Yueyin Pan, and Hossam Haick. ACS Nano 2020, XXXX, XXX, XXX-XXX DOI: https://doi.org/10.1021/acsnano.0c05657 Publication Date:August 18, 2020 Copyright © 2020 American Chemical Society

This paper is behind a paywall.

Sniffing out disease (Na-Nose)

The ‘artificial nose’ is not a newcomer to this blog. The most recent post prior to this is a March 15, 2016 piece about Disney using an artificial nose for art conservation. Today’s (Jan. 9, 2016) piece concerns itself with work from Israel and ‘sniffing out’ disease, according to a Dec. 30, 2016 news item in Sputnik News,

A team from the Israel Institute of Technology has developed a device that from a single breath can identify diseases such as multiple forms of cancer, Parkinson’s disease, and multiple sclerosis. While the machine is still in the experimental stages, it has a high degree of promise for use in non-invasive diagnoses of serious illnesses.

The international team demonstrated that a medical theory first proposed by the Greek physician Hippocrates some 2400 years ago is true, certain diseases leave a “breathprint” on the exhalations of those afflicted. The researchers created a prototype for a machine that can pick up on those diseases using the outgoing breath of a patient. The machine, called the Na-Nose, tests breath samples for the presence of trace amounts of chemicals that are indicative of 17 different illnesses.

A Dec. 22, 2016 Technion Israel Institute of Technology press release offers more detail about the work,

An international team of 56 researchers in five countries has confirmed a hypothesis first proposed by the ancient Greeks – that different diseases are characterized by different “chemical signatures” identifiable in breath samples. …

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 characterized by distinct chemical breath signatures. And 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 in 14 departments at 9 medical centers in 5 countries: Israel, France, the USA, Latvia and China.

The researchers 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. 13 chemical components were identified, in different compositions, in all 17 of the diseases.

According to Prof. Haick, “each of these diseases is characterized by a unique fingerprint, meaning a different composition of these 13 chemical components.  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 odor signatures are what enables us to identify the diseases using the technology that we developed.”

With a new technology called “artificially intelligent nanoarray,” developed by Prof. 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 analyze 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,” explain Prof. Haick and his previous Ph.D student, Dr. Morad Nakhleh, “and integration of the information provides detailed data about the unique breath signatures characteristic of the various diseases. Our system has detected and classified various diseases with an average accuracy of 86%.

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

“Breath is an excellent raw material for diagnosis,” said Prof. Haick. “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.”

Here’s a schematic of the study, which the researchers have made available,

Diagram: A schematic view of the study. Two breath samples were taken from each subject, one was sent for chemical mapping using mass spectrometry, and the other was analyzed in the new system, which produced a clinical diagnosis based on the chemical fingerprint of the breath sample. Courtesy: Tech;nion

There is also a video, which covers much of the same ground as the press release but also includes information about the possible use of the Na-Nose technology in the European Union’s SniffPhone project,

Here’s a link to and a citation for the paper,

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules by Morad K. Nakhleh, Haitham Amal, Raneen Jeries, Yoav Y. Broza, Manal Aboud, Alaa Gharra, Hodaya Ivgi, Salam Khatib, Shifaa Badarneh, Lior Har-Shai, Lea Glass-Marmor, Izabella Lejbkowicz, Ariel Miller, Samih Badarny, Raz Winer, John Finberg, Sylvia Cohen-Kaminsky, Frédéric Perros, David Montani, Barbara Girerd, Gilles Garcia, Gérald Simonneau, Farid Nakhoul, Shira Baram, Raed Salim, Marwan Hakim, Maayan Gruber, Ohad Ronen, Tal Marshak, Ilana Doweck, Ofer Nativ, Zaher Bahouth, Da-you Shi, Wei Zhang, Qing-ling Hua, Yue-yin Pan, Li Tao, Hu Liu, Amir Karban, Eduard Koifman, Tova Rainis, Roberts Skapars, Armands Sivins, Guntis Ancans, Inta Liepniece-Karele, Ilze Kikuste, Ieva Lasina, Ivars Tolmanis, Douglas Johnson, Stuart Z. Millstone, Jennifer Fulton, John W. Wells, Larry H. Wilf, Marc Humbert, Marcis Leja, Nir Peled, and Hossam Haick. ACS Nano, Article ASAP DOI: 10.1021/acsnano.6b04930 Publication Date (Web): December 21, 2016

Copyright © 2017 American Chemical Society

This paper appears to be open access.

As for SniffPhone, they’re hoping that Na-Nose or something like it will allow them to modify smartphones in a way that will allow diseases to be detected.

I can’t help wondering who will own the data if your smartphone detects a disease. If you think that’s an idle question, here’s an excerpt from Sue Halpern’s Dec. 22, 2016 review of two books (“Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil and “Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy” by Ariel Ezrachi and Maurice E. Stucke) for the New York Times Review of Books,

We give our data away. We give it away in drips and drops, not thinking that data brokers will collect it and sell it, let alone that it will be used against us. There are now private, unregulated DNA databases culled, in part, from DNA samples people supply to genealogical websites in pursuit of their ancestry. These samples are available online to be compared with crime scene DNA without a warrant or court order. (Police are also amassing their own DNA databases by swabbing cheeks during routine stops.) In the estimation of the Electronic Frontier Foundation, this will make it more likely that people will be implicated in crimes they did not commit.

Or consider the data from fitness trackers, like Fitbit. As reported in The Intercept:

During a 2013 FTC panel on “Connected Health and Fitness,” University of Colorado law professor Scott Peppet said, “I can paint an incredibly detailed and rich picture of who you are based on your Fitbit data,” adding, “That data is so high quality that I can do things like price insurance premiums or I could probably evaluate your credit score incredibly accurately.”

Halpern’s piece is well worth reading in its entirety.