In vivo evaluation of a mucoadhesive polymeric caplet for intravaginal anti-HIV-1 delivery and development of a molecular mechanistic model for thermochemical characterization

Context and objective: The aim of this study was to develop, characterize and evaluate a mucoadhesive caplet resulting from a polymeric blend (polymeric caplet) for intravaginal anti-HIV-1 delivery.

Materials and methods: Poly(lactic-co-glycolic) acid, ethylcellulose, poly(vinylalcohol), polyacrylic acid and modified polyamide 6, 10 polymers were blended and compressed to a caplet-shaped device, with and without two model drugs 3′-azido-3′-deoxythymidine (AZT) and polystyrene sulfonate (PSS). Thermal analysis, infrared spectroscopy and microscopic analysis were carried out on the caplets employing temperature-modulated DSC (TMDSC), Fourier transform infra-red (FTIR) spectrometer and scanning electron microscope, respectively. In vitro and in vivo drug release analyses as well as the histopathological toxicity studies were carried out on the drug-loaded caplets. Furthermore, molecular mechanics (MM) simulations were carried out on the drug-loaded caplets to corroborate the experimental findings.

Results and discussion: There was a big deviation between the T_g of the polymeric caplet from the T_g's of the constituent polymers indicating a strong interaction between constituent polymers. FTIR spectroscopy confirmed the presence of specific ionic and non-ionic interactions within the caplet. A controlled near zero-order drug release was obtained for AZT (20 d) and PSS (28 d). In vivo results, i.e. the drug concentration in plasma ranged between 0.012–0.332?mg/mL and 0.009–0.256?mg/mL for AZT and PSS over 1–28 d.

Conclusion: The obtained results, which were corroborated by MM simulations, attested that the developed system has the potential for effective delivery of anti-HIV-agents.     

Deep autoencoders for feature learning with embeddings for recommendations: a novel recommender system solution

Neural Computing and Applications - We propose “Deep Autoencoders for Feature Learning in Recommender Systems,” a novel discriminative model based on the incorporation of features from...     

Identifying crop water stress using deep learning models

Neural Computing and Applications - The identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several...     

Analytical Modeling of Surface Potential and Drain Current of Hetero-Dielectric DG TFET and Its Analog and Radio-Frequency Performance Evaluation

Semiconductors - In the present paper, analytical modeling of surface potential and drain current for hetero-dielectric double gate tunnel FET (HDG-TFET) has been done. The two dimensional (2D)...     

Analytical Modeling of a Triple Material Double Gate TFET with Hetero-Dielectric Gate Stack

Silicon - In this paper, we propose and develop an analytical model of a Triple material double gate Tunnel Field Effect Transistor (TM-DG TFET) with hetero-dielectric gate oxide stack comprising...     

High‐performance thermosets with tailored properties derived from methacrylated eugenol and epoxy‐based vinyl ester

A renewable chemical, eugenol, is methacrylated to produce methacrylated eugenol (ME) employing the Steglich esterification reaction without any solvent. The resulting ME is used as a low‐viscosity co‐monomer to replace styrene in a commercial epoxy‐based vinyl ester resin (VE). The volatility and viscosity of ME and styrene are compared. The effect of ME loading and temperature on the viscosity of the VE–ME resin is investigated. Moreover, the thermomechanical properties, curing extent and thermal stability of the fully cured VE–ME thermosets are systematically examined. The results indicate that ME is a monomer with low volatility and low viscosity, and therefore the incorporation of ME monomer in VE resins allows significant reduction of viscosity. Moreover, the viscosity of the VE–ME resin can be tailored by adjusting the ME loadings and processing temperature to meet commercial liquid molding technology requirements. The glass transition temperatures of VE–ME thermosets range from 139 to 199 °C. In addition, more than 95% of the monomer is incorporated and fixed in the crosslinked network structure of VE–ME thermosets. Overall, the developed ME monomer exhibits promising potential for replacing styrene as an effective low‐viscosity co‐monomer. The VE–ME resins show great advantages for use in polymer matrices for high‐performance fiber‐reinforced composites. This work is of great significance to the vinyl ester industry by providing detailed experimental support. © 2018 Society of Chemical Industry     

Large Area Self-Assembled Ultrathin Polyimine Nanofilms Formed at the Liquid–Liquid Interface Used for Molecular Separation

Separation membranes with higher molecular weight cut-offs are needed to separate ions and small molecules from a mixed feed. The molecular sieving phenomenon can be utilized to separate smaller species with well-defined dimensions from a mixture. Here, the formation of freestanding polyimine nanofilms with thicknesses down to ≈14 nm synthesized via self-assembly of pre-synthesized imine oligomers is reported. Nanofilms are fabricated at the water–xylene interface followed by reversible condensation of polymerization according to the Pieranski theory. Polyimine nanofilm composite membranes are made via transferring the freestanding nanofilm onto ultrafiltration supports. High water permeance of 49.5 L m^-2 h⁻¹ bar⁻¹ is achieved with a complete rejection of brilliant blue-R (BBR; molecular weight = 825 g mol⁻¹) and no more than 10% rejection of monovalent and divalent salts. However, for a mixed feed of BBR dye and monovalent salt, the salt rejection is increased to ≈18%. Membranes are also capable of separating small dyes (e.g., methyl orange; MO; molecular weight = 327 g mol⁻¹) from a mixed feed of BBR and MO. Considering a thickness of ≈14 nm and its separation efficiency, the present membrane has significance in separation processes.