Fratila, R. | Search bioRxiv

bioRxiv2024

Efficient targeting of heart lesions with cardiac myofibroblasts: Combined gene and cell therapy enhanced by magnetic steering

1The cardiac scar is a collagen-rich area, which is populated by myofibroblasts and has proven little amenable for therapeutic interventions. Herein, we have established an efficient targeting strategy for cardiac lesions by genetically manipulating embryonic cardiac myofibroblasts (mFB) in vitro, load the cells with magnetic nanoparticles and inject these into infarcted mouse hearts using magnetic steering. This yields strongly increased numbers ([~]4 fold compared to other cell types) of engrafted mFB. The injected mFB and endogenous myofibroblast (endoFB) population remain separate in the scar, but grafted mFB enhance the proliferation rate of endoFB by [~]4 fold. We also tested the functional impact of this approach by grafting lentiviral (LV)-transduced Connexin43 (Cx43) overexpressing mFB into the cardiac lesion. Prominent engraftment of Cx43+ mFB provides strong protection against post-infarct ventricular tachycardia (VT) in vivo, as VT incidence is reduced by [~]50 % at two and eight weeks after cell injection. Thus, ex vivo gene and subsequent in vivo cell therapy combined with magnetic steering of cardiac mFB enable efficient functional targeting of the cardiac scar.

physiology↗

bioRxiv2019

Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

BackgroundDepression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability.\n\nMethodsUsing STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature selection. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population.\n\nResultsOur deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% improvement in population remission rate (p= 0.01, C.I. 2.48% {+/-} .5%).\n\nConclusionOur model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from a number of treatment options. These models may have significant real-world clinical implications.

neuroscience↗