Determination of propofol by GC/MS and fast GC/MS-TOF in two cases of poisoning

Two cases of suspected acute and lethal intoxication caused by propofol were delivered by the judicial authority to the Department of Sciences for Health Promotion and Mother-Child Care in Palermo, Sicily. In the first case a female nurse was found in a hotel room, where she lived with her mother; four 10 mg/mL vials and two 20 mg/mL vials of propofol were found near the decedent along with syringes and needles. In the second case a male nurse was found in the operating room of a hospital, along with a used syringe. In both cases a preliminary systematic and toxicological analysis indicated the presence of propofol in the blood and urine. As a result, a method for the quantitative determination of propofol in biological fluids was optimized and validated using a liquid-liquid extraction protocol followed by GC/MS and fast GC/MS-TOF. In the first case, the concentration of propofol in blood was determined to be 8.1 \u3bcg/mL while the concentration of propofol in the second case was calculated at 1.2 \u3bcg/mL. Additionally, the tissue distribution of propofol was determined for both cases. Brain and liver concentrations of propofol were, respectively, 31.1 and 52.2 \u3bcg/g in Case 1 and 4.7 and 49.1 \u3bcg/g in Case 2. Data emerging from the autopsy findings, histopathological exams as well as the toxicological results aided in establishing that the deaths were due to poisoning, however, the manner of death in each were different: homicide in Case 1 and suicide in Case 2

Reconstructing nonparametric productivity networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra-and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown

Research Data Management 'Green Shoots' Pilot Programme, Final Reports

This document contains the final reports of six Research Data Management Green Shoots projects run at Imperial College in 2014