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Inadequate solid waste management (SWM) can lead to environmental contamination and human health risks. The health risks from poor SWM can vary based on specific practices and exposure pathways. Thus, it is necessary to adequately understand the local context. This information, however, is rarely available in low-resource settings, particularly in rural areas. A solid waste safety plan could be helpful in these settings for gathering necessary data to assess and minimize health risks. As a step in developing such a tool, a semi-quantitative health risk analysis of SWM practices in nine Ghanaian rural villages was undertaken. Data on SWM in each village were collected through qualitative field observations and semi-structured interviews with local stakeholders. SWM-related health risks were assessed using the collected data, similar case studies in the scientific literature and dialogue among an assembled team of experts. The analysis identified context-specific practices and exposure pathways that may present the most substantial health risks as well as targeted solutions for mitigation risks. A risk assessment matrix was developed to quantify SWM risks as low, medium, high, or very high based on the likelihood and severity of identified hazards. The highest SWM risks were identified from dumpsites and uncontrolled burying of solid waste. More specifically, a very high or high risk of infectious and vector-borne diseases from SWM in the villages was identified, both in the disposal of solid waste in dumpsites and uncontrolled burying of solid waste. Additionally, a very high or high risk of inhalation, ingestion or dermal contact with contaminants was found in the disposal of solid waste in dumpsites, open burning of waste and reuse of waste from dumpsites as compost. The results demonstrate the potential value of a solid waste safety plan and a parsimonious approach to collect key local data to inform its contents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Contrast medium (CM) use in computed tomography (CT) is limited by nephrotoxicity and possible allergic reactions. The purpose of this study is to introduce a tool for the diagnosis of abdominal aortic aneurysms (AAAs) by avoiding the use of CM., Methods: With and without CM CTs of patients with AAA were evaluated. A mathematical algorithm was implemented to allow visualization of the inner aortic lumen in the series without CM. The first step of the algorithm consisted in manually highlighting a squared region of interest (ROI) close to the target aortic area. The rest of the algorithm is completely automated and performs the following flow of operations: The "Kantorovich" algorithm is applied to the ROI for image enhancement. Then, a wavelet decomposition method is applied to identify the different frequency components of the image. Exploiting the wavelet decomposition, the system selects the low-frequency components of the image, corresponding to the major structures. Thresholding method, based on the analysis of the gray-level histogram, is then performed to extract the contours of the vessel. At this point, the extraction of the pervious area is completed. Final images were compared with the contrast enhanced scans, valued as gold standard. To validate the algorithm, an analysis of the results has been performed considering the following types of error: E n  = #m/#ROI ΔA = |1-(#CM/#CEX)| (# m = number of misclassified pixels; # ROI = number of pixels in the ROI; # CM = number of pixels belonging to the CM; #CEX = extracted areas). E n provided a measure on the pixels wrongly classified, and Δ A the difference of extracted areas., Results: The algorithm was applied to 233 CT images. Extracted images were compared with the ones with CM. Mean values of the numerical errors ranged from 0.12 to 0.17 for E n and 0.11 to 0.35 for Δ A . For all the 233 CT images in the analyzed sequences, the mean error was 0.14 (E n ) and 0.28 (ΔA)., Conclusions: The developed mathematical algorithm allows to detect the position of the vessel lumen and to extract its contours with a good accuracy. Our experience shows encouraging results and suggests a possible future clinical application to reduce CT CM use., (Copyright © 2019 Elsevier Inc. All rights reserved.)