Research round-up: Cleaning system, MRSA concerns and machine learning
We start with The University of Nottingham, which is developing an artificially-intelligent sensor system to clean food manufacturing equipment.
Cleaning can last up to five hours a day, account for 30% of energy and water use and leads to downtime and use of chemicals.
The research project, led by Martec of Whitwell, with the University of Nottingham and Loughborough University, has Innovate UK funding.
Cleaning options
Dr Nik Watson, assistant professor and chemical engineer specialising in food measurement systems, is leading the University of Nottingham team.
“To prevent product contamination, many food and drink manufacturers use a non-invasive, Clean-in-Place (CIP) system to wash inside food processing equipment without disassembling it. As CIP has to operate ‘blind’, it is designed for the worst case scenario. In daily use this often results in the over-cleaning of production lines.”
Researchers will design and build a lab-scale experimental rig and assess the potential for an artificial intelligence inspection system to measure how much food residue and microbial debris is left inside.
They will test a combination of ultrasonic sensing and optical fluorescence imaging technologies with existing detection methods for the best results.
The year-long feasibility study will develop software to process the sensor data results and generate algorithms for an AI-based monitoring system.
New MRSA strain capable of transmission from food to people
Methicillin-Resistant Staphylococcus aureus (MRSA) can spread to humans through eating or handling contaminated poultry, according to an international team of researchers.
The team led by Robert Skov, at Statens Serum Institut and Lance Price, at the Milken Institute School of Public Health at the George Washington University, showed that people with no exposure to livestock are becoming colonized and infected with a new strain of poultry-associated MRSA - most likely by eating or handling contaminated poultry meat.
“We’ve known for several years that people working directly with livestock are at increased risk for MRSA infections, but this is one of the first studies providing compelling evidence that everyday consumers are also potentially at risk,” said Price.
“We need to expand the number of pathogens that we test for in our food supply, and we need international leadership to reduce unnecessary use of antibiotics on industrial farms around the world.”
Routinely test for Salmonella
A team led by researchers from the University of Florida Institute of Food and Agricultural Sciences have improved on current methods to detect Salmonella in beef and chicken.
They artificially contaminated food with Salmonella and tested the samples using Salmonella-specific antibodies combined with a signal amplification technique.
The test found Salmonella present after 15 hours and removed other microorganisms. This is shorter than the two to three days to detect Salmonella in a culture.
The magnetic bead based immunoassay consists of immunomagnetic separation for target concentration with tyramide signal amplification to increase assay sensitivity.
Salmonella egg contamination
Eggs from small flocks of chickens are more likely to be contaminated with Salmonella enteritidis than those sold in grocery stores, according to Penn State College of Agricultural Sciences.
The conclusion was drawn from a six-month study last year in Pennsylvania. Researchers collected and tested more than 6,000 eggs from more than 200 selling points across the state.
Tests revealed that of the 240 selling points in the study, eggs from five (2%) were positive for Salmonella enteritidis.
The FDA requires shell-egg producers from farms with more than 3,000 chickens comply with the FDA Final Egg Rule. However, small flocks with fewer than 3,000 layer chickens are exempt.
Lead researcher Subhashinie Kariyawasam, microbiology section head at Penn State's Animal Diagnostic Laboratory, said the research highlights the potential risk posed by consumption of eggs produced by backyard and small layer flocks.
“These findings emphasize the importance of small-producer education on Salmonella enteritidis control measures and perhaps implementation of egg quality-assurance practices to prevent contamination of eggs produced by backyard and other small layer flocks.
“The bottom line is, if you buy your eggs from the small producers, you need to worry about Salmonella just as if you bought eggs produced by large flocks.”
Benefit from immune response
A UC Davis study has found pathogens in the intestinal tract cause harm because they benefit from immune system responses designed to repair damage to the intestinal lining caused by the bacteria.
A healthy large intestine is mostly free of oxygen and beneficial microbes thrive in this anaerobic environment. However, enteric pathogens such as E. coli in humans, need oxygen to survive.
The research has implications for developing treatment strategies to target factors that compromise the intestinal-lining function or bolster microbiota composition to offer resistance or assistance to invading pathogens.
“The finding is important because it explains how some enteric pathogens can manipulate mammalian cells to get the oxygen they need to breathe,” said Andreas Bäumler, a professor of medical microbiology and immunology at UC Davis School of Medicine and lead author.
“Enteric pathogens deploy virulence factors that damage the intestinal lining and cause diarrhea. To repair the damage, the body accelerates the division of epithelial cells that form the intestinal lining, which brings immature cells to the mucosal surface.
“These new cells contain more oxygen and wind up increasing oxygen levels in the large bowel, creating an environment that allows gut pathogens like E. coli to outcompete the anaerobic-loving resident microbes.”
Machine learning to prevent outbreaks
Researchers at the University of Edinburgh’s Roslin Institute have said machine learning can predict strains of bacteria likely to cause food poisoning outbreaks.
They used software that compares genetic information from bacterial samples isolated from animals and people and the work focused on harmful strains of E. coli bacteria.
The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. Once trained, the computer is able to predict whether an E. coli strain is likely to have come from a cow or a person.
Interventions to stop the spread of the disease – such as vaccines – could be targeted at herds with these strains to minimise the risk of outbreaks in people.
Researchers said the approach could be adapted to test samples of other types of bacteria isolated from animals to identify strains with the potential to cause human disease.
The study was funded by Food Standards Scotland and the Food Standards Agency and was a collaboration between The Roslin Institute, Public Health England (Colindale), the University of Glasgow and US Division of Agriculture.
Applying ‘big data’
Funding from Genome Canada will help Simon Fraser University (SFU) scientists develop ways to more effectively analyse, interpret and apply big data to improve key health-related issues.
Three SFU co-led projects are among 16 to share $4m through the Genome Canada and Canadian Institutes of Health Research Bioinformatics and Computational Biology competition.
Professor Fiona Brinkman is co-leading development of the Genomic Epidemiology Application Ontology (GenEpiO), with UBC, McMaster University and Genome BC.
“Ineffective tracking of infectious disease outbreaks, such as those involving foodborne illness, can have an enormous effect on the number of people - and countries - impacted,” said Brinkman.
“When outbreaks become global, however, it’s critical that data is shared across public health organizations securely and efficiently. Unfortunately data is often held in institution-specific formats, and sharing becomes difficult, time-consuming and costly.”
SFU researcher Cedric Chauve is co-leading a project to create PathOGIST, a computational framework.
The platform will enable public health workers to quickly classify pathogens into epidemiologically related groups, based on sequencing data, and generate interpreted genomic reports to inform actions.
Battle against sepsis
Kansas State University researchers developed an integrated mathematical and multi-agent-based model to simulate hepatic inflammatory response caused by Salmonella.
The model allows researchers to map interactions among cells, tissues and cytokines, which are small proteins important in cell signalling.
As more human data on sepsis becomes available, it may be developed into a visualization tool that can predict sepsis progression, test proposed treatments prior to preclinical experiments and eventually help in clinical decision-making.
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