Summary The study shows that deep machine learning can be utilized to more accurately identify erythema migrans rashes in early Lyme disease. Recognition of the EM rash is crucial to early diagnosis and treatment. Improved rash recognition using deep learning methodology to prescreen patient rash photos may help prevent later serious manifestations of Lyme disease. […]
This study uses a neuroimaging radiotracer with positron emission tomography (PET) to quantify cerebral glial activation in brains of patients with post-treatment Lyme disease. Results show elevated central nervous system immune activation in patients with PTLDS as compared to controls.
Our results indicate that substantial differences exist in how myositis is perceived by patients compared to healthcare providers, with different items prioritized.
Hydroxychloroquine sulfate is frequently used to treat patients with dermatomyositis. It has been associated with an increased risk of adverse skin reactions in these patients.
This study explores the prevalence and risk factors for “autonomic dysfunction” in scleroderma.
Lyme disease (Borrelia burgdorferi infection) is increasingly recognized as a significant worldwide illness. This study provides insights into important immune mechanisms involved in Borrelia burgdorferi clearance in human Lyme disease.
“Whether or not” and “how” to taper off the last bit of medication for patients with clinically stable ANCA-associated vasculitis are hotly debated topics in the vasculitis field.
This study evaluates overall incidence rates and demographic, seasonal, and geographic trends of first Lyme disease diagnosis among 384,652 Maryland Medicaid members enrolled from July 2004-June 2011 and represents the first analysis of claims data from publicly insured individuals.
Patients with Post-treatment Lyme Disease Syndrome (PTLDS) commonly complain of cognitive symptoms. The study found, in subsets of PTLDS patients, objective evidence of cognitive decline in verbal memory and processing speed, cognitive impairment, and sub-optimal engagement with testing.