Evaluation of a new imaging software for aortic endograft planning

Objective: The aim of this study was to evaluate a new 3D Workstation workflow (EVAR Assist, Advantage Windows, GE Healthcare, Chalfont, UK) (EA-AW) designed to simplify complex EVAR planning. Patients and methods: All pre-operative computed tomography (CT) scans of patients who underwent repair at our institution of a complex aortic aneurysm using fenestrated endovascular repair (f-EVAR) between January and September 2014, were reviewed. For each patient, imaging analysis (12 measures: aortic diameters and length and "clock position" of visceral artery) was performed on two different workstations: Aquarius (TeraRecon, San Mateo, CA, USA) and EA-AW. According to a standardized protocol, three endovascular surgeons experienced in aortic endograft planning, performed image analyses and data collection independently. We analyzed an internal assessment between observers (on the Aquarius 3DWS) and an external assessment comparing these results with the planning center (PC) data used to custom the fenestrated endografts of the patients enrolled in this study. Finally, we compared both 3DWS data to determine the accuracy and the reproducibility. A p-value < .05 was considered as statistically significant. Complete agreement between operators was defined as 1.0. Results: Intra and inter observer variability (interclass correlation coefficients - ICC: 0.81- .091) was very low and confirmed the reliability of our planners. The ICC comparison between EA-AW and Aquarius was excellent (> 0.8 for both), thus confirming the reproducibility and reliability of the new EA-AW application. Aortic and iliac necks diameters and lengths were similarly reported with both workstations. In our study, the mean difference in distance and orientation evaluation of target vessels evaluated by the two workstations was marginal and has no impact on clinical practice in term of device manufacturing. Conclusions: We showed that complex EVAR planning can be performed with this new dedicated 3D workstation workflow with a good reproducibility

NMR quality control of fragment libraries for screening

Fragment-based screening has evolved as a remarkable approach within the drug discovery process both in the industry and academia. Fragment screening has become a more structure-based approach to inhibitor development, but also towards development of pathway-specific clinical probes. However, it is often witnessed that the availability, immediate and long-term, of a high quality fragment-screening library is still beyond the reach of most academic laboratories. Within iNEXT (Infrastructure for NMR, EM and X-rays for Translational research), a EU-funded Horizon 2020 program, a collection of 782 fragments were assembled utilizing the concept of "poised fragments" with the aim to facilitate downstream synthesis of ligands with high affinity by fragment ligation. Herein, we describe the analytical procedure to assess the quality of this purchased and assembled fragment library by NMR spectroscopy. This quality assessment requires buffer solubility screening, comparison with LC/MS quality control and is supported by state-of-the-art software for high throughput data acquisition and on-the-fly data analysis. Results from the analysis of the library are presented as a prototype of fragment progression through the quality control process

Eruption type probability and eruption source parameters at Cotopaxi and Guagua Pichincha volcanoes (Ecuador) with uncertainty quantification

Future occurrence of explosive eruptive activity at Cotopaxi and Guagua Pichincha volcanoes, Ecuador, is assessed probabilistically, utilizing expert elicitation. Eight eruption types were considered for each volcano. Type event probabilities were evaluated for the next eruption at each volcano and for at least one of each type within the next 100 years. For each type, we elicited relevant eruption source parameters (duration, average plume height, and total tephra mass). We investigated the robustness of these elicited evaluations by deriving probability uncertainties using three expert scoring methods. For Cotopaxi, we considered both rhyolitic and andesitic magmas. Elicitation findings indicate that the most probable next eruption type is an andesitic hydrovolcanic/ash-emission (~ 26–44% median probability), which has also the highest median probability of recurring over the next 100 years. However, for the next eruption at Cotopaxi, the average joint probabilities for sub-Plinian or Plinian type eruption is of order 30–40%—a significant chance of a violent explosive event. It is inferred that any Cotopaxi rhyolitic eruption could involve a longer duration and greater erupted mass than an andesitic event, likely producing a prolonged emergency. For Guagua Pichincha, future eruption types are expected to be andesitic/dacitic, and a vulcanian event is judged most probable for the next eruption (median probability ~40–55%); this type is expected to be most frequent over the next 100 years, too. However, there is a substantial probability (possibly >40% in average) that the next eruption could be sub-Plinian or Plinian, with all that implies for hazard levels

A theoretical entropy score as a single value to express inhibitor selectivity

<p>Abstract</p> <p>Background</p> <p>Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.</p> <p>Results</p> <p>Here we propose a new theoretical entropy score that can be calculated from a set of IC₅₀data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC₅₀data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.</p> <p>Conclusions</p> <p>For quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.</p