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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Annane IntJInterMobileTech2019 13-4.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig0 Cardenia JofFoodDrugAnal2018 26-4.jpg|240px]]</div>
'''"[[Journal:Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges|Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges]]"'''
'''"[[Journal:Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L|Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L]]"'''


The principle constraints of mobile devices are their limited resources, including processing capability, storage space, and battery life. However, [[cloud computing]] offers a means of vast computing resources and services. With it a new idea emerged, the inclusion of cloud computing into mobile devices such as smartphones, tablet, and other personal digital assistants (PDA) to augment their capacities, providing a robust technology called mobile cloud computing (MCC). Although MCC has brought many advantages to mobile users, it also still suffers from the security and privacy issues of data while hosted on virtual machines (VM) on remote cloud’s servers. Currently, the eyes of security experts are turned towards the virtualization-based security techniques used either on the cloud or on mobile devices. The new challenge is to develop secure methods in order to authenticate highly sensitive digital content. ('''[[Journal:Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges|Full article...]]''')<br />
A routine method for determining [[wikipedia:Cannabinoid|cannabinoids]] in ''Cannabis sativa'' L. [[wikipedia:Inflorescence|inflorescence]], based on fast [[gas chromatography]] coupled to [[mass spectrometry]] (fast GC-MS), was developed and validated. To avoid the [[wikipedia:Decarboxylation|decarboxylation]] of the carboxyl group of cannabinoids, different derivatization approaches—i.e., silylation and esterification (diazomethane-mediated) reagents and solvents (pyridine or ethyl acetate)—were tested. The methylation significantly increased the signal-to-noise ratio of all carboxylic cannabinoids, except for cannabigerolic acid (CBGA). Since [[wikipedia:Diazomethane|diazomethane]] is not commercially available, is considered a hazardous reactive, and requires one-day synthesis by specialized chemical staff, the process of silylation was used along the entire validation of a routine method. The method gave a fast (total analysis time < 7.0 min) and satisfactory resolution (R > 1.1), with a good repeatability (intraday < 8.38%; interday < 11.10%) and sensitivity (LOD < 11.20 ng/mL). ('''[[Journal:Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L|Full article...]]''')<br />
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Revision as of 17:13, 9 December 2019

Fig0 Cardenia JofFoodDrugAnal2018 26-4.jpg

"Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L"

A routine method for determining cannabinoids in Cannabis sativa L. inflorescence, based on fast gas chromatography coupled to mass spectrometry (fast GC-MS), was developed and validated. To avoid the decarboxylation of the carboxyl group of cannabinoids, different derivatization approaches—i.e., silylation and esterification (diazomethane-mediated) reagents and solvents (pyridine or ethyl acetate)—were tested. The methylation significantly increased the signal-to-noise ratio of all carboxylic cannabinoids, except for cannabigerolic acid (CBGA). Since diazomethane is not commercially available, is considered a hazardous reactive, and requires one-day synthesis by specialized chemical staff, the process of silylation was used along the entire validation of a routine method. The method gave a fast (total analysis time < 7.0 min) and satisfactory resolution (R > 1.1), with a good repeatability (intraday < 8.38%; interday < 11.10%) and sensitivity (LOD < 11.20 ng/mL). (Full article...)

Recently featured:

▪ Design and refinement of a data quality assessment workflow for a large pediatric research network
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▪ Data sharing at scale: A heuristic for affirming data cultures
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