Application of artificial neural network for prediction of particle size in pharmaceutical cocrystallization using mechanochemical synthesis

Neural Computing and Applications - Milling by mechanical means is vital unit operation in pharmaceutical processing which can be used for controlling particle size reduction. This approach can be...     

A Flexi-PEGDA Upconversion Implant for Wireless Brain Photodynamic Therapy

Near-infrared (NIR) activatable upconversion nanoparticles (UCNPs) enable wireless-based phototherapies by converting deep-tissue-penetrating NIR to visible light. UCNPs are therefore ideal as wireless transducers for photodynamic therapy (PDT) of deep-sited tumors. However, the retention of unsequestered UCNPs in tissue with minimal options for removal limits their clinical translation. To address this shortcoming, biocompatible UCNPs implants are developed to deliver upconversion photonic properties in a flexible, optical guide design. To enhance its translatability, the UCNPs implant is constructed with an FDA-approved poly(ethylene glycol) diacrylate (PEGDA) core clad with fluorinated ethylene propylene (FEP). The emission spectrum of the UCNPs implant can be tuned to overlap with the absorption spectra of the clinically relevant photosensitizer, 5-aminolevulinic acid (5-ALA). The UCNPs implant can wirelessly transmit upconverted visible light till 8 cm in length and in a bendable manner even when implanted underneath the skin or scalp. With this system, it is demonstrated that NIR-based chronic PDT is achievable in an untethered and noninvasive manner in a mouse xenograft glioblastoma multiforme (GBM) model. It is postulated that such encapsulated UCNPs implants represent a translational shift for wireless deep-tissue phototherapy by enabling sequestration of UCNPs without compromising wireless deep-tissue light delivery.     

Probing the Substrate Promiscuity of Isopentenyl Phosphate Kinase as a Platform for Hemiterpene Analogue Production

Isoprenoids are a large class of natural products with wide-ranging applications. Synthetic biology approaches to the manufacture of isoprenoids and their new-to-nature derivatives are limited due to the provision in nature of just two hemiterpene building blocks for isoprenoid biosynthesis. To address this limitation, artificial chemo-enzymatic pathways such as the alcohol-dependent hemiterpene (ADH) pathway serve to leverage consecutive kinases to convert exogenous alcohols into pyrophosphates that could be coupled to downstream isoprenoid biosynthesis. To be successful, each kinase in this pathway should be permissive of a broad range of substrates. For the first time, we have probed the promiscuity of the second enzyme in the ADH pathway—isopentenyl phosphate kinase from Thermoplasma acidophilum—towards a broad range of acceptor monophosphates. Subsequently, we evaluate the suitability of this enzyme to provide unnatural pyrophosphates and provide a critical first step in characterizing the rate-limiting steps in the artificial ADH pathway.     

Anaerobic biological treatment of phenolic wastewater at 15-18 degrees C

Low-temperature, or psychrophilic (<20 degrees C) anaerobic digestion has been proven feasible for the mineralisation of simple wastewaters. In this study, hybrid expanded granular sludge bed-anaerobic filter (EGSB-AF) bioreactors were used to evaluate the feasibility of psychrophilic digestion for the treatment of phenol-containing wastewater. Efficient chemical oxygen demand and phenol removal were observed at organic and phenol loading rates of 5 kg COD m(-3)d(-1) and 0.4-1.2 kg phenol m(-3)d(-1) (400-1200 mg phenol [l wastewater](-1)), respectively. There was no long-term accumulation of volatile fatty acids in the reactor systems. Methanogenic activity was developed under psychrophilic conditions but anaerobic methane-producing populations remained mesophilic throughout the trial of 415 days.     

Accuracy and optimal sampling in Monte Carlo solution of population balance equations

Implementation of a Monte Carlo simulation for the solution of population balance equations (PBEs) requires choice of initial sample number (N₀), number of replicates (M), and number of bins for probability distribution reconstruction (n). It is found that Squared Hellinger Distance, H², is a useful measurement of the accuracy of Monte Carlo (MC) simulation, and can be related directly to N₀, M, and n. Asymptotic approximations of H² are deduced and tested for both one‐dimensional (1‐D) and 2‐D PBEs with coalescence. The central processing unit (CPU) cost, C, is found in a power‐law relationship, , with the CPU cost index, b, indicating the weighting of N₀ in the total CPU cost. n must be chosen to balance accuracy and resolution. For fixed n, M × N₀ determines the accuracy of MC prediction; if b > 1, then the optimal solution strategy uses multiple replications and small sample size. Conversely, if 0 < b < 1, one replicate and a large initial sample size is preferred. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2394–2402, 2015     

Applications of percolation theory to fungal spread with synergy

There is increasing interest in the use of the percolation paradigm to analyse and predict the progress of disease spreading in spatially structured populations of animals and plants. The wider utility of the approach has been limited, however, by several restrictive assumptions, foremost of which is a strict requirement for simple nearest-neighbour transmission, in which the disease history of an individual is influenced only by that of its neighbours. In a recent paper, the percolation paradigm has been generalized to incorporate synergistic interactions in host infectivity and susceptibility, and the impact of these interactions on the invasive dynamics of an epidemic has been demonstrated. In the current paper, we elicit evidence that such synergistic interactions may underlie transmission dynamics in real-world systems by first formulating a model for the spread of a ubiquitous parasitic and saprotrophic fungus through replicated populations of nutrient sites and subsequently fitting and testing the model using data from experimental microcosms. Using Bayesian computational methods for model fitting, we demonstrate that synergistic interactions are necessary to explain the dynamics observed in the replicate experiments. The broader implications of this work in identifying disease-control strategies that deflect epidemics from invasive to non-invasive regimes are discussed.