Believe it or not, a tropical blood parasite native to Latin America could be harmful to Canadians. Infectious diseases like malaria or Zika may have dominated recent headlines but Chagas - the "Kissing Bug" disease - is in the spotlight following the publication of a new case study in the Canadian Medical Association Journal (CMAJ).
Tropical and laboratory medicine experts from Winnipeg and Montreal warn natives of specific Central and South American nations and their offspring are at risk of contracting Chagas disease - even after they have moved to Canada. The study reports on a family case of transmissions from mother to unborn children, raising questions over prevention and diagnosis of Chagas disease in Canada, where thousands of individuals live with potentially undetected infection.
Chagas disease is caused by a parasite called Trypanosoma cruzi, which is mostly found in Latin America and, occasionally, in southern parts of the United States. It spreads through the bite of triatomine - bloodsucking insects targeting a person's face, referred to as "Kissing bugs." The parasite is transmitted via the bugs' feces: The insects defecate while feeding, allowing the parasite to move on to its new host. The disease can spread via transmission from mother to child during pregnancy and from infected blood transfusions or organ transplantation.
"Chagas disease is a real public health problem due to the transmission from mother to child (baby) up to at least three generations," says co-author Dr. Momar Ndao, a scientist from the Infectious Diseases and Immunity in Global Health Program at the Research Institute of the McGill University Health Centre (RI-MUHC), and an associate professor in the Department of Medicine at McGill University. "As Chagas disease is not a notifiable communicable disease in Canada, there are little data on the number of undiagnosed, untreated cases."
A urine test for tuberculosis could make it much easier to identify the disease and treat it before it kills.
There were more than 10 million new TB infections in 2016, and the condition killed 1.7 million people. In around 40 per cent of cases, the infection isn’t identified until symptoms become obvious.
TB is currently diagnosed using a skin test, or by culturing bacteria from a person’s sputum. But both these methods take days to give results, and can only be performed by trained microbiologists.
Now Alessandra Luchini, of George Mason University in Virginia, and her team have developed a urine test for TB that gives results in 12 hours. The test detects a certain sugar that coats the surface of TB bacteria, which usually ends up in infected people’s urine in low concentrations.
The test uses tiny molecular cages embedded with a special dye that can catch and trap these sugar molecules. This makes the test capable of detecting the sugar at low concentrations, making it the technique as much as 1000 times more accurate as previous methods for detecting TB in urine.
When the team tested their technique, they correctly identified 48 people with TB. Luchini now wants to make the test easier to use, and test it on thousands more people. If all goes well, it could be available within three years, she says.
Journal reference: Science Translational Medicine
Scientists in Boston have developed an automated artificial intelligence (AI)-guided microscopy system that can help diagnose serious bloodstream infections (BSIs) quickly and accurately. The technology, which uses a trained convolutional neural network (CNN) to recognize the different shapes and distribution of pathogenic bacteria, could help to speed diagnosis and potentially save patient lives, as well as address the current lack of trained microbiology technologists, suggest its developers at Harvard Medical School and Beth Israel Deaconess Medical Center (BIDMC).
“This marks the first demonstration of machine learning in the diagnostic area," comments James Kirby, M.D., director of the Clinical Microbiology Laboratory at BIDMC, and associate professor of pathology at Harvard Medical School. "With further development, we believe this technology could form the basis of a future diagnostic platform that augments the capabilities of clinical laboratories, ultimately speeding the delivery of patient care." The researchers report on the technology in the Journal of Clinical Investigation, in a paper entitled “Automated Interpretation of Blood Culture Gram Stains using a Deep Convolutional Neural Network.”
The mortality rate from BSIs can be up to 40%, and every day’s delay in starting appropriate treatment increases the risk of death, so fast identification of the causative bacteria is critical. Initial diagnosis is based on the Gram stain smear, but human interpretation of Gram-stained slide images is "labor and time intensive, and highly operator-dependent," the authors explain. “With consolidation of hospital systems, increasing workloads, and potential unavailability of highly trained microbiologists on site, automated image collection paired with computational interpretation of Gram stains to augment and complement manual testing would provide benefit.”
Dr. Kirby’s team now reports on the development of a trained CNN-based model that is designed to overcome some of the current technical difficulties associated with automating Gram stain analysis. The approach combines an automated slide imaging platform equipped with a 40x air objective and a trained CNN-based model that can recognize bacterial morphology.