Production and characterization of carbamazepine nanocrystals by electrospraying for continuous pharmaceutical manufacturing

In this paper, an electrospray technique followed by annealing at high temperatures was developed to produce nanocrystals of carbamazepine (CBZ), a poorly water-soluble drug, for continuous pharmaceutical manufacturing process. Electrospraying solutions of CBZ in methanol obeys the expected scaling law of current, which is I ∼ Q[superscript 1/2] (I, electrical current; Q, flow rate), for liquids with sufficiently high conductivity and viscosity. Lower flow rates during electrospraying were preferred to produce smaller diameters of monodisperse, dense CBZ nanoparticles. CBZ nanoparticles were predominantly amorphous immediately after electrospraying. Crystallization of CBZ nanoparticles was accelerated by annealing at high temperatures. CBZ nanocrystals with the most stable polymorph, form III, were obtained by annealing at 90°C, which is above the transition temperature, 78°C, for the enantiotropic CBZ form III and form I. The solubility and dissolution rates of CBZ nanocrystals increased significantly as compared with those of CBZ bulk particles. Therefore, electrospray technology has the potential to produce pharmaceutical dosage forms with enhanced bioavailability and can readily be integrated in a continuous pharmaceutical manufacturing process.Novartis-MIT Center for Continuous Manufacturin

Nanofiltration of hormone mimicking trace organic contaminants

The removal mechanisms of three hormone mimicking organic compounds by nanofiltration (NF) membranes have been examined. Two NF membranes having different pore size were used in laboratory-scale nanofiltration experiments with feed solutions spiked with a hormone mimicking compound ¾ nonylphenol, tert-butyl phenol, or bisphenol A. Retention of the compounds was determined at various solution chemistries, namely aqueous solution pH, ionic strength, and presence of natural organic matter. The nanofiltration behavior of the selected hormone mimicking compounds appears similar to that of natural hormones as reported in our previous work. While the solution pH can dramatically influence the retention of hormone mimicking compounds by a loose NF membrane, ionic strength does not affect the nanofiltration of such contaminants. However, in the presence of natural organic matter in the feed solution, ionic strength appears to play a significant role in solute-solute and solute-membrane interactions, resulting in increased retention due to partitioning of the hormone mimicking compounds onto organic matter at a higher ionic strength

Evaluation of fertilizer-drawn forward osmosis for coal seam gas reverse osmosis brine treatment and sustainable agricultural reuse

© 2017 Elsevier B.V. The fertilizer-drawn forward osmosis (FDFO) was investigated for treating coal seam gas (CSG) produced water to generate nutrient rich solution for irrigation. Its performance was evaluated and compared with reverse osmosis (RO) in terms of specific energy consumption (SEC) and nutrient concentrations in the final product water. The RO-FDFO hybrid process was developed to further improve FDFO. The results showed that FDFO has the lowest SEC followed by the RO-FDFO and RO processes. The final nutrient concentration simulation demonstrated that the RO-FDFO hybrid process has lower final concentration, higher maximum recovery and lower nutrient loss than the stand alone FDFO process. Therefore, it was suggested that the RO-FDFO is the most effective treatment option for CSG produced water as well as favourable nutrient supply. Lastly, membrane fouling mechanism was examined in CSG RO brine treatment by FDFO, and the strategies for controlling fouling were critically evaluated. KNO3 exhibited the highest flux decline corresponding to the highest reverse salt flux, while the most severe membrane scaling was observed with calcium nitrate, primarily due to the reverse transport of calcium ions. To control membrane fouling in FDFO process, both physical flushing and chemical cleaning were examined. Membrane cleaning with citric acid of 5% resulted in a complete flux recovery

Research on injury compensation and health outcomes : ignoring the problem of reverse causality led to a biased conclusion

Objective\ud \ud This study highlights the serious consequences of ignoring reverse causality bias in studies on compensation-related factors and health outcomes and demonstrates a technique for resolving this problem of observational data.\ud \ud Study Design and Setting\ud \ud Data from an English longitudinal study on factors, including claims for compensation, associated with recovery from neck pain (whiplash) after rear-end collisions are used to demonstrate the potential for reverse causality bias. Although it is commonly believed that claiming compensation leads to worse recovery, it is also possible that poor recovery may lead to compensation claims—a point that is seldom considered and never addressed empirically. This pedagogical study compares the association between compensation claiming and recovery when reverse causality bias is ignored and when it is addressed, controlling for the same observable factors.\ud \ud Results\ud \ud When reverse causality is ignored, claimants appear to have a worse recovery than nonclaimants; however, when reverse causality bias is addressed, claiming compensation appears to have a beneficial effect on recovery, ceteris paribus.\ud \ud Conclusion\ud \ud To avert biased policy and judicial decisions that might inadvertently disadvantage people with compensable injuries, there is an urgent need for researchers to address reverse causality bias in studies on compensation-related factors and health

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008–2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors’ keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer’s, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products