What does “triple-washed” or “pre-washed” mean on that bag of baby spinach? Not much, according to engineers at the University of California at Riverside. They discovered that small peaks and valleys in baby spinach leaves could be a key reason why there have been numerous bacterial outbreaks involving leafy green vegetables.
Currently, disinfectant is put into the rinse water – not specifically applied to the leaf surface. The researchers in the Bourns College of Engineering found that because of the varied topography of the spinach leaf, bacteria may continue to live, grow, spread and contaminate other leaves and surfaces.
“In a sense the leaf is protecting the bacteria and allowing it to spread,” said Nichola Kinsinger, a post-doctoral researcher working on the project. “It was surprising to discover how the leaf surface formed micro-environments that reduce the bleach concentration – and in this case, the very disinfection processes intended to clean, remove, and prevent contamination was found to be the potential pathway to amplifying foodborne outbreaks.” ucr.edu
Algorithm interprets breathing difficulties
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Researchers from N.C. State have developed an algorithm that can interpret the wheezing of patients with breathing difficulties – and give medical providers information about what’s happening in the lungs.
The algorithm can assess the onset time, pitch and magnitude (volume) of wheezing sounds to give health care professionals information about the condition of the lungs.
Here’s how the system is eventually supposed to work: Sensors that monitor breathing transmit data to a smart device, such as a smartphone. That information is then run through the algorithm. If the algorithm finds that there is a breathing problem, the smart device could then notify the patient and his or her medical provider.
The work was done by Saba Emrani and Hamid Krim, researchers in a National Science Foundation research center based at N.C. State. ncsu.edu
Smartphones can track flu on campus
New methods for analyzing personal health and lifestyle data captured through wearable devices or smartphone apps can help identify college students at risk of catching the flu, say researchers at Duke University and UNC-Chapel Hill.
With help from a mobile app that monitors who students interact with and when, epidemiologist Allison Aiello of UNC and statistician Katherine Heller of Duke developed a model that over time enables them to predict the spread of influenza from one person to the next.
This approach gives a personalized daily forecast for each patient, Heller said. In theory, doctors could use such data to identify and alert at-risk students before they get sick or start to feel symptoms, or to encourage them to stay at home to avoid infecting other students.
The model was applied in a study of roughly 100 students at the University of Michigan. For 10 weeks during the 2013 flu season, the students carried Google Android smartphones with built-in software, iEpi, that used Wi-Fi, Bluetooth and GPS technology to monitor where they went and who they came in contact with from moment to moment. The students also recorded their symptoms every week online. Students who reported coughing and fever, chills or aches provided throat swabs to determine whether they had a cold or the flu.
The model then returned the odds that each student would spread or contract the flu on a given day, and identified the personal health habits – such as hand-washing or getting a flu shot – that might help them beat the odds or hasten their recovery. duke.edu