Delp, S. | Search bioRxiv

bioRxiv2018

Reversible temporally-specific inhibition of muscle using a light-activated chloride channel

Achieving reversible, temporally specific inhibition of motor neurons has the potential to revolutionize treatment of disorders marked by muscle hyperactivity. Current treatment strategies are inadequate - surgical interventions are irreversible and pharmaceutical interventions have off-target effects. Optogenetic strategies for inhibiting muscle activity have theoretical promise; however, trafficking and expression problems have prevented translatable optogenetic suppression of muscle activity. Here, we exploit recent innovations in opsin engineering to demonstrate virally mediated, temporally specific optogenetic inhibition of motor neurons and muscle activity in vivo. We show that intra-muscular injection of adeno-associated virus serotype 6 can drive expression of the inhibitory channelrhodopsin mutant iC++ in both immunodeficient and wild-type mice. Illumination of the sciatic nerve in wild-type mice resulted in 64.8% inhibition of evoked twitch force. Optical excitation during tetanic stimulation in wild-type mice resulted in 59.1% inhibition at 10 Hz and 55.4% at 25 Hz. The extent of optogenetic inhibition was titratable, and ranged from 0% to 78.4% as illumination intensity was changed from 1 mW to 20 mW/mm2. These results could have therapeutic applicability to disorders such as spasticity, hypertonia and urinary incontinence, and provide a new tool to neuroscientists and muscle physiologists wishing to reversibly inhibit motor neuron activity in vivo.

neuroscience↗

bioRxiv2018

Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

bioinformatics↗