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Biology subjects

McDaniel, C.

Publications and source records attributed to McDaniel, C..

2 recordsLinked to original sources

Superior Efficacy of Combination Antibiotic Therapy versus Monotherapy in a Mouse Model of Lyme Disease

Lyme disease (LD) results from the most prevalent tick-borne infection in North America, with over 476,000 estimated cases annually. The disease is caused by Borrelia burgdorferi (Bb) which transmits through the bite of Ixodid ticks. Most cases treated soon after infection are resolved by a short course of oral antibiotics. However, 10-20% of patients experience chronic symptoms because of delayed or incomplete treatment, a condition called Post-Treatment Lyme Disease (PTLD). Some Bb persists in PTLD patients after the initial course of antibiotics and an effective treatment to eradicate the persistent Bb is needed. Other organisms that cause persistent infections, such as M. tuberculosis, are cleared using a combination of therapies rather than monotherapy. A group of Food and Drug Administration (FDA)-approved drugs efficacious against Bb were used in monotherapy or in combination in mice infected with Bb. Different methods of detection were used to assess the efficacy of the treatments in the infected mice including culture, xenodiagnosis, and molecular techniques. None of the monotherapies eradicated persistent Bb. However, 4 dual combinations (doxycycline + ceftriaxone, dapsone + rifampicin, dapsone + clofazimine, doxycycline + cefotaxime) and 3 triple combinations (doxycycline + ceftriaxone+ carbomycin, doxycycline + cefotaxime+ loratadine, dapsone+ rifampicin+ clofazimine) eradicated persistent Bb infections. These results suggest that combination therapy should be investigated in preclinical studies for treating human Lyme disease.

microbiology↗

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinsons Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC=0.87), as well as tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95) severity.

bioengineering↗