Biosynthesis and Characterization of Silver Nanoparticles by Aspergillus Species

Currently, researchers turn to natural processes such as using biological microorganisms in order to develop reliable and ecofriendly methods for the synthesis of metallic nanoparticles. In this study, we have investigated extracellular biosynthesis of silver nanoparticles using four Aspergillus species including A. fumigatus, A. clavatus, A. niger, and A. flavus. We have also analyzed nitrate reductase activity in the studied species in order to determine the probable role of this enzyme in the biosynthesis of silver nanoparticles. The formation of silver nanoparticles in the cell filtrates was confirmed by the passage of laser light, change in the color of cell filtrates, absorption peak at 430 nm in UV-Vis spectra, and atomic force microscopy (AFM). There was a logical relationship between the efficiencies of studied Aspergillus species in the production of silver nanoparticles and their nitrate reductase activity. A. fumigatus as the most efficient species showed the highest nitrate reductase activity among the studied species while A. flavus exhibited the lowest capacity in the biosynthesis of silver nanoparticles which was in accord with its low nitrate reductase activity. The present study showed that Aspergillus species had potential for the biosynthesis of silver nanoparticles depending on their nitrate reductase activity

Validation of a method for identifying nursing home admissions using administrative claims

<p>Abstract</p> <p>Background</p> <p>Currently there is no standard algorithm to identify whether a subject is residing in a nursing home from administrative claims. Our objective was to develop and validate an algorithm that identifies nursing home admissions at the resident-month level using the MarketScan Medicare Supplemental and Coordination of Benefit (COB) database.</p> <p>Methods</p> <p>The computer algorithms for identifying nursing home admissions were created by using provider type, place of service, and procedure codes from the 2000 – 2002 MarketScan Medicare COB database. After the algorithms were reviewed and refined, they were compared with a detailed claims review by an expert reviewer. A random sample of 150 subjects from the claims was selected and used for the validity analysis of the algorithms. Contingency table analysis, comparison of mean differences, correlations, and t-test analyses were performed. Percentage agreement, sensitivity, specificity, and Kappa statistics were analyzed.</p> <p>Results</p> <p>The computer algorithm showed strong agreement with the expert review (99.9%) for identification of the first month of nursing home residence, with high sensitivity (96.7%), specificity (100%) and a Kappa statistic of 0.97. Weighted Pearson correlation coefficient between the algorithm and the expert review was 0.97 (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>A reliable algorithm indicating evidence of nursing home admission was developed and validated from administrative claims data. Our algorithm can be a useful tool to identify patient transitions from and to nursing homes, as well as to screen and monitor for factors associated with nursing home admission and nursing home discharge.</p