9 research outputs found

    Mapping vulnerable urban areas affected by slow-moving landslides using Sentinel-1InSAR data

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    Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of vulnerable buildings affected by slow-moving landslides, based on two parameters: (1) the deformation rate associated to each building, measured from Sentinel-1 SAR data, and (2) the building damage generated by the landslide movement and recorded during a field campaign. We apply this method to Arcos de la Frontera, a monumental town in South Spain affected by a slow-moving landslide that has caused severe damage to buildings, forcing the evacuation of some of them. Our results show that maximum deformation rates of 4 cm/year in the line-of-sight (LOS) of the satellite, affects La Verbena, a newly-developed area, and displacements are mostly horizontal, as expected for a planar-landslide. Our building damage assessment reveals that most of the building blocks in La Verbena present moderate to severe damages. According to our vulnerability scale, 93% of the building blocks analysed present high vulnerability and, thus, should be the focus of more in-depth local studies to evaluate the serviceability of buildings, prior to adopting the necessary mitigation measures to reduce or cope with the negative consequences of this landslide. This methodology can be applied to slow-moving landslides worldwide thanks to the global availability of Sentinel-1 SAR data.Postprint (published version

    Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

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    Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

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    Séptimo desafío por la erradicación de la violencia contra las mujeres del Institut Universitari d’Estudis Feministes i de Gènere "Purificación Escribano" de la Universitat Jaume

    Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

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    Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

    Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence

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    InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.Peer ReviewedPostprint (published version

    Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence

    No full text
    InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.Peer Reviewe

    Effects of pre-operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

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