Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach.

Neuroscience has contributed to uncover the mechanisms underpinning substance use disorders (SUD). The next frontier is to leverage these mechanisms as active targets to create more effective interventions for SUD treatment and prevention. Recent large-scale cohort studies from early childhood are generating multiple levels of neuroscience-based information with the potential to inform the development and refinement of future preventive strategies. However, there are still no available well-recognized frameworks to guide the integration of these multi-level datasets into prevention interventions. The Research Domain Criteria (RDoC) provides a neuroscience-based multi-system framework that is well suited to facilitate translation of neurobiological mechanisms into behavioral domains amenable to preventative interventions. We propose a novel RDoC-based framework for prevention science and adapted the framework for the existing preventive interventions. From a systematic review of randomized controlled trials using a person-centered drug/alcohol preventive approach for adolescents, we identified 22 unique preventive interventions. By teasing apart these 22 interventions into the RDoC domains, we proposed distinct neurocognitive trajectories which have been recognized as precursors or risk factors for SUDs, to be targeted, engaged and modified for effective addiction prevention.

Thermosensitive molecularly imprinted poly(1-vinyl-2-pyrrolidone/methyl methacrylate/N-vinylcaprolactam) for selective extraction of imatinib mesylate in human biological fluid.

The efficiency of a molecularly imprinted polymer as a selective packing material for the solid-phase extraction of imatinib mesylate sorption was investigated. The molecularly imprinted polymer was prepared using N,N'-methylenebisacrylamide as a cross-linker agent, N-vinylcaprolactam as a thermo-sensitive monomer, 1-vinyl-2-pyrrolidone and methyl methacrylate as functional monomers, azobisisobutyronitrile as an initiator and imatinib mesylate as a template. The drug-imprinted polymer was identified by Fourier transform infrared spectroscopy, thermogravimetric analysis, elemental analysis, and scanning electron microscopy. It was found that this polymer can be used for determination of trace levels of imatinib mesylate with a recovery percentage that could reach over 90%. Furthermore, the synthesized molecularly imprinted polymer indicated higher selectivity towards imatinib mesylate than other compounds. From isotherm study, the equilibrium adsorption data of imatinib mesylate by imprinted polymer were analyzed by Langmuir, Freundlich, and Temkin isotherm models. The developed method was used for determination of imatinib mesylate in human fluid samples by high performance liquid chromatography with excellent results.

Adsorption of Amido Black 10B from aqueous solution using polyaniline/SiO2 nanocomposite: Experimental investigation and artificial neural network modeling.

The present work focused on the performance of Polyaniline/SiO2 nanocomposite for removing Amido Black 10B dye from aqueous solution. The effect of different variables, such as adsorption time, the mass of adsorbent, solution pH and initial dye concentration was studied and also was optimized by an Artificial Neural Network (ANN) method. Lagergren, pseudo-second order, Intra-particle Diffusion, Elovich and Boyd models were tested to track the kinetics of the adsorption process. The experimental data were fitted to different two-parameter, and three-parameter isotherm models, namely, Langmuir, Freundlich, Temkin, D-R, Hill, Sips and Redlich-Peterson models, and their validity was examined. The results showed that the dye adsorption process was well described by Redlich-Peterson isotherm model. Thermodynamic studies revealed that the adsorption of Amido Black 10B onto Polyaniline/SiO2 nanocomposite was endothermic. The comparison of the adsorption efficiencies obtained by the ANN model and the experimental data evidenced that the ANN model could estimate the behavior of the Amido Black 10B dye adsorption process under various conditions.