Bridging the Gap: An Osteopathic Primary Care-Centered Approach to Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is a deadly and incurable disease typically diagnosed in early childhood. Presently, the delay between a caregiver's initial concern and the primary care physician obtaining creatine kinase levels-the most important screening test-is more than a year. It is imperative to diagnose DMD as soon as possible because early treatment has the potential to double the patient's lifespan. In addition, because of geographic and economic disadvantages, multidisciplinary DMD treatment centers are not readily available to all patients. Therefore, the challenge of early diagnosis and treatment coordination rests with the primary care physician. The present review provides osteopathic primary care physicians with current and relevant information regarding DMD diagnosis and management.

Model-independent phenotyping of C. elegans locomotion using scale-invariant feature transform.

To uncover the genetic basis of behavioral traits in the model organism C. elegans, a common strategy is to study locomotion defects in mutants. Despite efforts to introduce (semi-)automated phenotyping strategies, current methods overwhelmingly depend on worm-specific features that must be hand-crafted and as such are not generalizable for phenotyping motility in other animal models. Hence, there is an ongoing need for robust algorithms that can automatically analyze and classify motility phenotypes quantitatively. To this end, we have developed a fully-automated approach to characterize C. elegans' phenotypes that does not require the definition of nematode-specific features. Rather, we make use of the popular computer vision Scale-Invariant Feature Transform (SIFT) from which we construct histograms of commonly-observed SIFT features to represent nematode motility. We first evaluated our method on a synthetic dataset simulating a range of nematode crawling gaits. Next, we evaluated our algorithm on two distinct datasets of crawling C. elegans with mutants affecting neuromuscular structure and function. Not only is our algorithm able to detect differences between strains, results capture similarities in locomotory phenotypes that lead to clustering that is consistent with expectations based on genetic relationships. Our proposed approach generalizes directly and should be applicable to other animal models. Such applicability holds promise for computational ethology as more groups collect high-resolution image data of animal behavior.