Host glycosylation of immunoglobulins impairs the immune response to acute Lyme disease.

Background: Lyme disease is caused by the bacteria Borreliella burgdorferi sensu lato (Bb) transmitted to humans from the bite of an infected Ixodes tick. Current diagnostics for Lyme disease are insensitive at the early disease stage and they cannot differentiate between active infections and people with a recent history of antibiotic-treated Lyme disease.

Methods: Machine learning technology was utilized to improve the prediction of acute Lyme disease and identify sialic acid and galactose sugar structures (N-glycans) on immunoglobulins associated specifically at time points during acute Lyme disease time.

The Spectrum of Erythema Migrans in Early Lyme Disease: Can We Improve Its Recognition?

Background and objective Diagnosis of early Lyme disease (LD) often relies on clinical recognition of the skin lesion, erythema migrans (EM), a diagnostic sign of disease when laboratory testing is insensitive. Because EM can present in morphologically distinct forms, its recognition by clinicians can be challenging. This study aimed to characterize the clinical spectrum of lesions in patients presenting with suspected early LD in an ambulatory care setting to identify features that might help clinicians to be better prepared to recognize EM lesions.

The Lyme Disease Biobank: Characterization of 550 Patient and Control Samples from the East Coast and Upper Midwest of the United States.

Lyme disease (LD) is an increasing public health problem. Current laboratory testing is insensitive in early infection, the stage at which appropriate treatment is most effective in preventing disease sequelae. The Lyme Disease Biobank (LDB) collects samples from individuals with symptoms consistent with early LD presenting with or without erythema migrans (EM) or an annular, expanding skin lesion and uninfected individuals from areas of endemicity.