Non-operative management of blunt abdominal trauma. Is it safe and feasible in a district general hospital?

<p>Abstract</p> <p>Background</p> <p>To evaluate the feasibility and safety of non-operative management (NOM) of blunt abdominal trauma in a district general hospital with middle volume trauma case load.</p> <p>Methods</p> <p>Prospective protocol-driven study including 30 consecutive patients who have been treated in our Department during a 30-month-period. Demographic, medical and trauma characteristics, type of treatment and outcome were examined. Patients were divided in 3 groups: those who underwent immediate laparotomy (OP group), those who had a successful NOM (NOM-S group) and those with a NOM failure (NOM-F group).</p> <p>Results</p> <p>NOM was applied in 73.3% (22 patients) of all blunt abdominal injuries with a failure rate of 13.6% (3 patients). Injury severity score (ISS), admission hematocrit, hemodynamic status and need for transfusion were significantly different between NOM and OP group. NOM failure occurred mainly in patients with splenic trauma.</p> <p>Conclusion</p> <p>According to our experience, the hemodynamically stable or easily stabilized trauma patient can be admitted in a non-ICU ward with the provision of close monitoring. Splenic injury, especially with multiple-site free intra-abdominal fluid in abdominal computed tomography, carries a high risk for NOM failure. In this series, the main criterion for a laparotomy in a NOM patient was hemodynamic deterioration after a second rapid fluid load.</p

Review of solar energetic particle models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.</p

4D U-Nets for Multi-Temporal Remote Sensing Data Classification

Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multi-temporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multi-dimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-&agrave;-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme

TripleGeo: an ETL Tool for Transforming Geospatial Data into RDF Triples

Integrating data from heterogeneous sources has led to the development of Extract-Transform-Load (ETL) systems and methodologies, as a means of addressing modern interoperability challenges. A few such tools have been available for converting between geospatial formats, but none specifically addressing the emerging needs of geospatially-enabled RDF stores. In this paper, we introduce TripleGeo, an open-source ETL utility that can extract geospatial features from various sources and transform them into triples for subsequent loading into RDF stores. TripleGeo can directly access both geometric representations and thematic attributes either from standard geographic formats or widely used DBMSs. It can also reproject input geometries on-the-fly into a different Coordinate Reference System, before exporting the resulting triples into a variety of notations. Most importantly, TripleGeo supports the recent GeoSPARQL standard endorsed by the Open GeoSpatial Consortium, although it can extract geometries into other vocabularies as well. This tool has been validated against OpenStreetMap layers with millions of geometries, opening up perspectives to add more functionality and to address much bigger data volumes. 1

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme

Dyspnea in patients treated with P2Y12 receptor antagonists: insights from the GReek AntiPlatElet (GRAPE) registry

In ‘real life’ acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) and receiving contemporary antiplatelet treatment, data on dyspnea occurrence and impact on persistence with treatment are scarce. In a prospective, multicenter, cohort study, ACS patients undergoing PCI were recruited into the GReekAntiPlatElet (GRAPE) registry. During 1-year follow up, overall, 249/1989 (12.5%) patients reported dyspnea, more frequently at 1-month and decreasing thereafter. Multivariate analysis showed that ticagrelor administration (n = 738) at discharge was associated with the occurrence of dyspnea: Odds ratio 2.46 (95% confidence interval, CI, 1.87–3.25), p < 0.001. Older age, lower hematocrit, and prior bleeding event were also associated with dyspnea reports. Persistence, switching, and cessation rates were 68.3%, 20.9%, and 10.8% vs 76.7%, 12.5%, and 10.9% among patients reporting dyspnea compared with those who did not, p for trend = 0.002. In conclusion, in ACS patients undergoing PCI and treated with a P2Y12 receptor antagonist, dyspnea occurs commonly, particularly when ticagrelor is administered. Non-persistence with antiplatelet agents at discharge is more frequently observed among dyspnea-reporters

Greater decline of acute stroke admissions compared with acute coronary syndromes during COVID-19 outbreak in Greece: Cerebro/cardiovascular implications amidst a second wave surge

Background and purpose: A remarkable decline in admissions for acute stroke and acute coronary syndrome (ACS) has been reported in countries severely hit by the COVID-19 pandemic. However, limited data are available from countries with less COVID-19 burden focusing on concurrent stroke and ACS hospitalisation rates from the same population. Methods: The study was conducted in three geographically and demographically representative COVID-19 referral university hospitals in Greece. We recorded the rate of stroke and ACS hospital admissions during a 6-week period of the COVID-19 outbreak in 2020 and compared them with the rates of the corresponding period in 2019. Results: We found a greater relative reduction of stroke admissions (51% [35 vs. 71]; incidence rate ratio [IRR]: 0.49, p = 0.001) compared with ACS admissions (27% [123 vs. 168]; IRR: 0.73, p = 0.009) during the COVID-19 outbreak (p = 0.097). Fewer older (&gt;65 years) patients (stroke: 34.3% vs. 45.1%, odds ratio [OR]: 0.64, p = 0.291; ACS: 39.8% vs. 54.2%, OR: 0.56, p = 0.016) were admitted during the COVID-19 compared with the control period. Conclusions: Hospitalisation rates both for stroke and ACS were reduced during the COVID-19 outbreak in a country with strict social distancing measures, low COVID-19 incidence and low population mortality. Lack of triggers for stroke and ACS during social distancing/quarantining may explain these observations. However, medical care avoidance attitudes among cerebro/cardiovascular patients should be dissipated amidst the rising second COVID-19 wave. © 2020 European Academy of Neurolog