Gadolinium-Enhanced Extracranial MRA Prior to Mechanical Thrombectomy Is Not Associated With an Improved Procedure Speed

Objectives: To assess whether performing a pre-intervention gadolinium-enhanced extracranial magnetic resonance angiogram (MRA) in addition to intracranial vascular imaging is associated with improved thrombectomy time metrics.Methods: Consecutive patients treated by MT at a large comprehensive stroke center between January 2012 and December 2017 who were screened using pre-intervention MRI were included. Patients characteristics and procedural data were collected. Univariate and multivariate analysis were performed to compare MT speed, efficacy, complications, and clinical outcomes between patients with and without pre-intervention gadolinium-enhanced extracranial MRA.Results: A total of 912 patients were treated within the study period, including 288 (31.6%) patients with and 624 (68.4%) patients without extracranial MRA. Multivariate analysis showed no significant difference between groups in groin puncture to clot contact time (RR = 0.93 [0.85–1.02], p = 0.14) or to recanalization time (RR = 0.92 [0.83–1.03], p = 0.15), rates of successful recanalization (defined as a mTICI 2b or 3, RR = 0.93 [0.62–1.42], p = 0.74), procedural complications (RR = 0.81 [0.51–1.27], p = 0.36), and good clinical outcome (defined by a mRS ≤ 2 at 3 months follow-up, RR = 1.05 [0.73–1.52], p = 0.79).Conclusion: Performing a pre-intervention gadolinium-enhanced extracranial MRA in addition to non-contrast intracranial MRA at stroke onset does not seem to be associated with a delay or shortening of procedure times

Combinations of single-top-quark production cross-section measurements and vertical bar f(LV)V(tb)vertical bar determinations at root s=7 and 8 TeV with the ATLAS and CMS experiments

This paper presents the combinations of single-top-quark production cross-section measurements by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at = 7 and 8 TeV corresponding to integrated luminosities of 1.17 to 5.1 fb(-1) at = 7 TeV and 12.2 to 20.3 fb(-1) at = 8 TeV. These combinations are performed per centre-of-mass energy and for each production mode: t-channel, tW, and s-channel. The combined t-channel cross-sections are 67.5 +/- 5.7 pb and 87.7 +/- 5.8 pb at = 7 and 8 TeV respectively. The combined tW cross-sections are 16.3 +/- 4.1 pb and 23.1 +/- 3.6 pb at = 7 and 8 TeV respectively. For the s-channel cross-section, the combination yields 4.9 +/- 1.4 pb at = 8 TeV. The square of the magnitude of the CKM matrix element V-tb multiplied by a form factor f(LV) is determined for each production mode and centre-of-mass energy, using the ratio of the measured cross-section to its theoretical prediction. It is assumed that the top-quark-related CKM matrix elements obey the relation |V-td|, |V-ts| << |V-tb|. All the |f(LV)V(tb)|(2) determinations, extracted from individual ratios at = 7 and 8 TeV, are combined, resulting in |f(LV)V(tb)| = 1.02 +/- 0.04 (meas.) +/- 0.02 (theo.). All combined measurements are consistent with their corresponding Standard Model predictions.Peer reviewe

Data_Sheet_1_Lipid rafts disruption by statins negatively impacts the interaction between SARS-CoV-2 S1 subunit and ACE2 in intestinal epithelial cells.PDF

The causative agent of the COVID-19 pandemic, SARS-CoV-2, is a virus that targets mainly the upper respiratory tract. However, it can affect other systems such as the gastrointestinal (GI) tract. Therapeutic strategies for this virus are still inconclusive and understanding its entry mechanism is important for finding effective treatments. Cholesterol is an important constituent in the structure of cellular membranes that plays a crucial role in a variety of cellular events. In addition, it is important for the infectivity and pathogenicity of several viruses. ACE2, the main receptor of SARS-CoV-2, is associated with lipid rafts which are microdomains composed of cholesterol and sphingolipids. In this study, we investigate the role of statins, lipid-lowering drugs, on the trafficking of ACE2 and the impact of cholesterol modulation on the interaction of this receptor with S1 in Caco-2 cells. The data show that fluvastatin and simvastatin reduce the expression of ACE2 to variable extents, impair its association with lipid rafts and sorting to the brush border membrane resulting in substantial reduction of its interaction with the S1 subunit of the spike protein. By virtue of the substantial effects of statins demonstrated in our study, these molecules, particularly fluvastatin, represent a promising therapeutic intervention that can be used off-label to treat SARS-CoV-2.</p

Smart decision support for maintenance logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Smart decision support for maintenance logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Smart Decision Support for Maintenance Logistics

Spare parts demand forecasting has been an interesting area of research over the past years. This area of research is getting increasing attention due to the enormous costs associated with managing and owning spare parts, while spare parts availability is critical in industries where any process blockage will cost thousands up to millions of euros. To this extent, various spare parts forecasting techniques exist to decrease the stock level, without sacrificing the required service availability. These techniques mostly use historical demand data (time series) to forecast future demand. Lately, the fast technological development in sensors and communication technologies has paved the path towards real-time condition monitoring, which in turn enabled Condition-Based maintenance (CBM). In CBM, real-time condition information allows planners to anticipate future component failures, and maintenance actions are planned accordingly. However, few researchers have considered exploiting condition information in spare parts decision-making. Doing so could be beneficial because real-time condition data gives real-time information about future spare parts demand. We propose an inventory policy that exploits the condition information and pooling effect in spare parts decision-making. It anticipates the need for a spare to perform the maintenance action by ordering one when the degradation crosses an Order Threshold smaller than the part replacement threshold. We hypothesize that the proposed policy will exploit both condition information and the pooling effect to reduce the average stock level. The policy's two decision variables are the initial stock level and the Order Threshold. We evaluate this policy using Discrete Event Simulation (DES), and we develop a simulation-based optimization algorithm that explores the search space intelligently to find the optimal parameters. We show that the proposed proactive policy reduces the average stock level by 35\% on average

Chemical diversity and antimicrobial activity of the essential oils of four Apiaceae species growing wild in Lebanon

International audienc