Liposomes are attractive vehicles for localized delivery of antibiotics. There exists, however, a gap in knowledge when it comes to achieving high liposomal loading efficiencies for antibiotics. To address this issue, we investigated three antibiotics of clinical relevance against staphylococcal infections with different hydrophilicity and chemical structure, namely, vancomycin hydrochloride, teicoplanin, and rifampin. We categorized the suitability of different encapsulation techniques on the basis of encapsulation efficiency, lipid requirement (important for avoiding lipid toxicity), and mass yield (percentage of mass retained during the preparation process). The moderately hydrophobic (teicoplanin) and highly hydrophobic (rifampin) antibiotics varied significantly in their encapsulation load (max 23.4 and 15.5%, respectively) and mass yield (max 74.1 and 71.8%, respectively), favoring techniques that maximized partition between the aqueous core and the lipid bilayer or those that produce oligolamellar vesicles, whereas vancomycin hydrochloride, a highly hydrophilic molecule, showed little preference to any of the protocols. In addition, we report significant bias introduced by the choice of analytical method adopted to quantify the encapsulation efficiency (underestimation of up to 24% or overestimation by up to 57.9% for vancomycin and underestimation of up to 61.1% for rifampin) and further propose ultrafiltration and bursting by methanol as the method with minimal bias for quantification of encapsulation efficiency in liposomes. The knowledge generated in this work provides critical insight into the more practical, albeit less investigated, aspects of designing vesicles for localized antibiotic delivery and can be extended to other nanovehicles that may suffer from the same biases in analytical protocols.

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The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses nonlinear dynamics of high-dimensional dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary architectures and arbitrary dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from simple spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses nonlinear dynamics of high-dimensional dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary architectures and arbitrary dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from simple spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.