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Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

Background: Management of chronic hepatitis C (CHC) has significantly accelerated in the last few years. Currently, second generation direct acting antivirals (DAAs) promise clearance of infection in most of patients. Here we present the results of the first analysis carried out on data of Lazio clinical network for DAAs. Methods: The study was designed as a multicenter cohort: a) to assess the evolution of treatment during the first 24 months of the activity of the Clinical Network; b) to report overall efficacy of treatments; c) to analyze potential factors associated with lack of virological response at 12 weeks after therapy (SVR12); d) to evaluate the variation of ALT at baseline and 12 weeks after therapy in those who achieved SVR12 in comparison to those who did not. Analyses of efficacy were carried out with multilevel mixed effect logistic regression model. ALT temporal variation was assessed by mixed effect model mixed models with random intercept at patient's level and random slope at the level of the time; i.e. either before or after therapy. Results: Between 30 December 2014 and 31 December 2016 5279 patients started a DAA treatment; of those, 5127 (in 14 clinical centers) had completed the 12-week follow-up. Overall proportion of SVR12 was 93.41% (N = 4780) with no heterogeneity between the 14 clinical centers. Interruption as the consequence of severe side effect was very low (only 23 patients). Unadjusted analysis indicates that proportion of SVR12 significantly changes according to patient's baseline characteristics, however after adjusting for potential confounders only adherence to current guidelines, stage of liver diseases, gender, transplant and HIV status were independently associated with the response to therapy. Analysis of ALT temporal variation showed that ALT level normalized in most, but not, all patients who achieved SVR12. Conclusion: Our study confirmed the extraordinary efficacy of DAAs outside clinical trials. The advantage of DAAs