Seek and ye shall find: Sapovirus sickens 650 in Sweden, 2016

A foodborne outbreak of gastroenteritis with more than 650 suspected cases occurred in April 2016 in Sollentuna, Sweden. It originated in a school kitchen serving a total of 2,700 meals daily.

Initial microbiological testing (for Campylobacter, Salmonella, Shigella, Yersinia, Giardia, Cryptosporidium, Entamoeba histolytica, adeno-, astro-, noro-, rota- and sapovirus) of stool samples from 15 symptomatic cases was negative, despite a clinical presentation suggestive of calicivirus.

Analyses of the findings from both the Sollentuna municipality environmental team and a web-based questionnaire suggested that the source of the outbreak was the salad buffet served on 20 April, although no specific food item could be identified.

Subsequent electron microscopic examination of stool samples followed by whole genome sequencing revealed a variant of sapovirus genogroup V. The virus was not detected using standard PCR screening. This paper describes the epidemiological outbreak investigation and findings leading to the discovery.

Investigation of a foodborne outbreak of gastroenteritis in a school canteen revealed a variant of sapovirus  genogroup V not detected by standard PCR, sollentuna, Sweden, 2016

Eurosurveillance, vol 22, issue 22, 01 June 2017, M Hergens, J Nederby Öhd, E Alm , HH Askling, S Helgesson, M Insulander, N Lagerqvist, B Svenungsson, M Tihane, T Tolfvenstam, P Follin,

http://dx.doi.org/10.2807/1560-7917.ES.2017.22.22.30543

http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=22808

 

Expediting detection of pathogens in food supply

Angelo Gaitas, a research assistant professor at Florida International University’s Electrical and Computer Engineering Department, along with Gwangseong Kim, a research scientist, are commercializing a device that reduces the screening process of foods to just a few hours at the same cost as current devices.

FIU says that if you have ever suffered from food poisoning, you will appreciate why it is so important to inspect food before it reaches the consumer. Food producers have to check for bacteria and signs of contamination before they are able to ship out any perishable food. Some common bacteria that can lead to foodborne illnesses include E.coli, salmonella and listeria. In fact, according to the Centers for Disease Control, each year, one in six Americans gets sick by consuming contaminated foods or beverages, that is forty-eight million people, out of whom 128,000 are hospitalized.

Typically, the inspection process, which involves putting samples in a solution and placing it in an incubator to see if bacteria grows, takes anywhere from 18 hours to several days. The reason is that it takes time for bacteria to grow at detectable levels. Current detection techniques are limited – you may need about 1,000 to a million bacteria present, depending on the technique, in a small volume before bacteria can be successfully detected. To reach that level, it takes time.

With this new device, food producers are able to run the whole solution through a smaller container inside the incubator oven. Antibodies in the device capture the target bacteria. This procedure allows bacteria to be concentrated in a smaller volume enabling same day detection.

“We are focused on helping food producers reduce storage cost and get fresher food to consumers,” Gaitas says. “We are addressing a major and well documented need in a very large market. There are about 1.2 billion food tests conducted worldwide and about 220 million tests in the United States.”

By shortening the detection time by one day, the team believes that the device can save the food industry billions. For example, meat producers, as a collective industry, could save up to $3 billion in storage costs by shortening the detection to one day. This device can also be used to expedite the detection of bloodborne illnesses such as sepsis and viral infections; however, currently the commercial focus is on food due to the lower barriers to entry.

Gaitas formed a company, Kytaro Inc – an FIU startup – which spent the last few years creating and testing the device and publishing the results in scientific journals. Besides Gaitas and Kim, the company has been employing FIU undergraduates.

FIU notes that this April, with the support of Henry Artigues of the Office of Research and Economic Development and Shekhar Bhansali, chair of the Electrical and Computer Engineering Department, Kytaro was recognized as one of “40 Best University Startups 2017” at the University Startups Conference and Demo Day in Washington, D.C. About 200 startups applied to the national competition.

Men who stare at goats – and use spinach as bomb detectors

A team of scientists from the Massachusetts Institute of Technology (MIT) embedded carbon nanotubes in spinach leaves which emitted a signal when they detected nitroaromatics — a chemical compound used in landmines and other explosives.

spinach-bomb-detectionThrough the nanotubes, which are one ten-thousandth the diameter of a human hair, the plant can detect the chemicals through the air and groundwater.

Researchers also applied a solution of nanoparticles to the underside of the leaves and placed sensors into a leaf layer (known as the mesophyll) where most photosynthesis takes place.

To read the signals the plants give off, researchers shine a laser on the leaves which prompts the carbon nanotubes to emit a near-infrared fluorescent light.

That light is picked up by using an infrared camera connected to a Raspberry Pi, a credit-card-sized computer, similar to the computer used in a smartphone.

The Raspberry Pi then sends an email to the phone, alerting the owner to the presence and size of an explosive.

By engineering these plants to act as chemical sensors, scientists can perform monitoring tasks in public spaces and identify potential terrorism threats at mass-attended events, said Michael Strano, professor of chemical engineering at MIT.

“They could also be used on the periphery of a chemical plant and even fracking sites.”

Plants are ideal for this purpose as they have extensive root networks to monitor groundwater, are self-repairing, and are naturally adaptive to where they exist.

“If you think of taking your iPhone or a piece of electronics outside and having it adapt to the temperature changes, it’s actually an engineering challenge,” said Professor Strano.

men-who-stare-at-goats“We look at the plant for a great starting point for technology.

“It’s amazing it hasn’t been explored for this purpose.”

The researchers can pick up the warning signal from about one metre away, but are working to increase that distance.

As well as spinach, researchers used rocket and watercress as chemical sensors, choosing to use plants that were commonly available.

“We wanted to show that these techniques work with plants found in the wild or a nursery, rather than using genetically-engineered plants,” Professor Strano said.

By using plants that already exist in the wild, the need to create new organisms which may have problems surviving is eliminated.

A.I. might prevent the next E. coli outbreak

Tonya Riley of Inverse reports that artificial intelligence is already well on its way to being the future of food service, but what if it could also do things like prevent foodborne illnesses, such as E. coli?

cow-poop2Researchers at University of Edinburgh say they’ve designed  software to do just that. The A.I. compares the genetic signatures of E. coli samples that have caused infection in humans to bacterial samples from humans and animals. The technology will allow researchers to identify deadly strains of E. coli before the threat becomes an outbreak.

“Our findings indicate that the most dangerous E. coli O157 strains may in fact be very rare in the cattle reservoir, which is reassuring,” University of Edinburgh Professor David Gally said in a press release. “The study highlights the potential of machine learning approaches for identifying these strains early.”

E.coli strains can normally live in human and animal guts without complications, but strains like E.coli O157 can cause infection. The strain is much more deadly in humans than in cattle, where the bacteria serves to collect toxins that need disposed. The team predicts that the O157 strain is present in about 10 percent of cattle.

Advancements in cellular engineering will make it easier for researchers to detect bacteria that is harmful to humans, but let’s not forget E. coli isn’t all bad. Air Force researchers have shown that the bacteria may be key to controlling robots through biological means.

Researchers plan on using the software on test samples of other animal-borne toxins, such as salmonella in order to identify strains with the potential to cause human disease.

Food fraud detection: Chinese team develops new method for rapid authentication of edible oils and screening of gutter oils

The Food Safety and Technology Research Centre under the Department of Applied Biology and Chemical Technology of The Hong Kong Polytechnic University (PolyU) has developed a new method for rapid authentication of edible oils and screening of gutter oils. Authentication of edible oils has been a long-term issue in food safety, and becomes particularly important with the emergence and widespread use of gutter oils in recent years. However, the conventional analytical approach for edible oils is not only labor intensive and time consuming, but also fails to provide a versatile solution for screening of gutter oils. By setting up a simple analytical protocol and a spectral library of edible oils, the new approach is able to determine the authenticity of a labeled edible oil sample and hence screened gutter oils within five minutes.

1-polyudevelopThe conventional approach for edible oil authentication involves labor-intensive and time-consuming sample pretreatment and the subsequent chromatographic separation to separate complex sample mixture before mass spectrometric detection, a commonly used technology for identification and quantitation of chemical compounds. The whole process takes a few hours to analyze one sample. On the other hand, identification of gutter oils mainly involves detection of certain food residue markers or toxic and carcinogenic chemicals in the sample. However, due to the vast diversity of gutter oils, and the fact that target compounds could be removed by processing, a universal strategy to screen gutter oils is not available at present.

PolyU researchers have developed a simplified method for direct analysis of edible oils using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). In the new MALDI-MS approach, only simple sample preparation, automatic data acquisition and simple data processing are involved. High quality and highly reproducible MALDI-MS spectra results can be obtained using this method, and a preliminary spectral database of labeled edible oils available in the market has been set up. Since different types of edible oils have different MALDI-MS spectral patterns, the authenticity of an edible oil sample can then be determined within five minutes by comparing its MALDI-MS spectrum with those of its labeled oil in the established database. Since this method is capable of authenticating edible oils, it also enables a rapid screening of gutter oils, given fraudulent mislabeling is a common feature of gutter oils.

The related paper has been recently published on Analytica Chimica Acta, a leading journal in Analytical Chemistry. The research team will establish a more complete MALDI spectral library of various edible oils in the coming two years, and improve the library searching technique. In addition, more testing of edible oil samples with different MALDI-MS equipment will be carried out to further validate the new approach.

The Eyes have it? Iowa researchers study retinal scans as early detection method for mad cow disease

New research from Iowa State University shows that a fatal neurological disease in cows can be detected earlier by examining the animal’s retinas.

mad.cows.mother's.milkBovine spongiform encephalopathy (BSE), known more commonly as mad cow disease, is an untreatable neurodegenerative disorder caused by misfolded brain proteins known as prions. Classic BSE incubates for years before producers or veterinarians notice symptoms, usually discovered when the animal can no longer stand on its own.

But Heather Greenlee, an associate professor of biomedical sciences in Iowa State’s College of Veterinary Medicine, said studying the retinas of cattle can identify infected animals up to 11 months before they show signs of illness.

“The retina is part of the central nervous system,” Greenlee said. “Essentially, it’s the part of the brain closest to the outside world, and we know the retina is changed in animals that have prion diseases.”

In collaboration with Justin Greenlee’s group at the U.S. Department of Agriculture’s National Animal Disease Center, she recently published findings in the peer-reviewed academic journal PLOS ONE. She began studying how the retina relates to prion diseases in 2006, and the experiments that led to her most recent publication began in 2010.

The experiments utilize electroretinography and optical coherence tomography, noninvasive technologies commonly used to assess the retina. Greenlee said cows infected with BSE showed marked changes in retinal function and thickness.

The results have implications for food safety, and Greenlee said the screening methods used in her research could be adopted for animals tagged for import or export as a means of identifying BSE sooner than conventional methods.

Greenlee said she’s also looking at how similar diseases in other species affect the retina. For instance, she’s conducting experiments to find out if retinal tissue may be a valid means of surveillance for chronic wasting disease in deer.

She said she isn’t ready to publish her results, but the data gathered so far looks promising.

The research also may contribute to faster diagnosis of Alzheimer’s disease and Parkinson’s disease in humans, both of which are caused by proteins folding incorrectly.

“Our goal is to develop our understanding of the retina to monitor disease progression and to move diagnoses up earlier,” Greenlee said. “We think this research has the potential to improve diagnosis for a range of species and a range of diseases.”

MIT phage-based bacterial detection for produce

Ever wonder why fruits and vegetables sometimes hit the shelves contaminated by pathogenic bacteria such as listeria, E. coli, and salmonella?

According to Tim Lu, an assistant professor of electrical engineering and biological engineering at MIT, it boils down to the inefficient bacteria-750px-PhageExterior.svgdetection assays used in the food industry. In some cases, these aren’t accurate or speedy enough — sometimes taking several days to catch contaminated produce.

But now Lu’s startup, Sample6, is commercializing an advanced assay platform that “lights up” pathogenic bacteria for quick detection, with the ability to detect only a few bacteria. 

Based on Lu’s graduate school research at MIT, the assay uses biological particles called bacteriophages, or phages, which only target bacteria. In Sample6’s case the assay is engineered to inject pathogenic bacteria — specifically, listeria — with an enzyme that reprograms the bacteria to shine very brightly.  

To use the commercial assay, called the Bioillumination Platform, factory workers simply swab samples with a sponge, wait for the phages to do their work, and run the sample through a machine that detects any light emitted. Results can be plugged into the company’s software, which tracks contaminated products and can provide analytics on whether contamination correlates with certain days, people, or suppliers.  

Data analysis allows researchers to predict disease outbreaks

Researchers tracking social media and Web searches have, according to USA Today, detected outbreaks of the flu and rare diseases in Latin America by up to two weeks before they were reported by local news media or government health agencies.

Working at a series of universities and companies around the country, the researchers are part of a program led by the Intelligence Advanced Research Projects Agency (IARPA) that is aimed at anticipating critical social.media.likesocietal events, such as disease outbreaks, violent uprisings or economic crises before they appear in the news.

“The goal is to use publicly available information to predict events, such as political violence, disease outbreaks and economic crises,” said Jason Matheny, program manager of IARPA’s Open Source Indicators program. “We’re using leading indicators like social media, Web search trends, Wikipedia in order to identify the events. We’re looking at flu outbreaks or other signs of unrest in a population.”

IARPA’s goal, Matheny said, is to inform U.S. policymakers about major events early enough to make more of a difference. Too often, he said, public announcements of disease outbreaks come too late. Intelligence analysts with access to a system able to eliminate the clutter that’s common in open source data may be able to get a jump on disease outbreaks or other problems.

IARPA is the intelligence community’s version of the Defense Advanced Research Projects Agency (DARPA), which performs much of the military’s research into technology to make better weapons or improve medical treatments.