Determination of the amount (i.e., area and volume) of soil losses due to erosional landforms, especially collapsed pipes, plays a considerable role in different decision-making approaches. Further, mapping the spatia...
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Determination of the amount (i.e., area and volume) of soil losses due to erosional landforms, especially collapsed pipes, plays a considerable role in different decision-making approaches. Further, mapping the spatial distribution and predicting the volumetric and areal losses of collapsed pipes (CPs) are essential for supporting ecosystem health. The study was conducted in relation to the area and volume of CPs and their related cova-riables. It focused on the estimation of soil losses due to collapsed pipes using unmanned aerial vehicle (UAV) images as well as field covariates at the Chatal Watershed, Golestan Province, Iran. A total of 481 soil samples were collected from homogeneous units with an area of approximately 1,410 ha. The potential relationship between the area/volume of collapsed pipes and land use, several topographic attributes (i.e., altitude, slope, and aspect), and soil properties, including soil stability, soil organic matter, clay, silt, and sand contents were analyzed using five distance-basedmethods (i.e., kernel density (KD), average nearest neighbor (ANN), spatial autocorrelation, hotspot analysis (HSA), and ordinary least square (OLS) analysis. The average nearest neighbor (Ratio = 0.12, Z score =-20.30, p-value < 0.05) and Moran space solidarity (Moran index = 0.258, Z score = 5.50, p-value < 0.05) showed the cluster distribution of area and volume of CPs. Hot spots and cold spots in the southwestern part of the study area were identified using KD and HSA. The relationship between existing in-dependent and dependent variables (area of CPs) using regression analysis of OLS showed that slope and aggregate stability (> 2.5 standard deviation) had the highest positive relationship with the dependent variable. Regarding the volume of CPs, land use (especially agricultural lands) had the strongest relationship with the dependent variable. Thus, geometrical characteristics of collapsed pipes can be applied as a quantitative indi-cator for the
The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DN...
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The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information. In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R’s principle. less
Purpose: Minimally invasive trans-catheter aortic valve implantation (TAVI) has emerged as a treatment of choice for high-risk patients with severe aortic stenosis. However, the planning of TAVI procedures would great...
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Purpose: Minimally invasive trans-catheter aortic valve implantation (TAVI) has emerged as a treatment of choice for high-risk patients with severe aortic stenosis. However, the planning of TAVI procedures would greatly benefit from automation to speed up, secure and guide the deployment of the prosthetic valve. We propose a hybrid approach allowing the computation of relevant anatomical measurements along with an enhanced visualization. Material and methods: After an initial step of centerline detection and aorta segmentation, model-based and statistical-based methods are used in combination with 3 D active contour models to exploit the complementary aspects of these methods and automatically detect aortic leaflets and coronary ostia locations. Important anatomical measurements are then derived from these landmarks. Results: A validation on 50 patients showed good precision with respect to expert sizing for the ascending aorta diameter calculation (2.2 +/- 2.1 mm), the annulus diameter (1.31 +/- 0.75 mm), and both the right and left coronary ostia detection (1.96 +/- 0.87 mm and 1.80 +/- 0.74 mm, respectively). The visualization is enhanced thanks to the aorta and aortic root segmentation, the latter showing good agreement with manual expert delineation (Jaccard index: 0.96 +/- 0.03). Conclusion: This pipeline is promising and could greatly facilitate TAVI planning. [GRAPHICS] .
The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied...
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The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method. based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed. less
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