Introduction: There are few scores for mortality prediction in acute respiratory distress syndrome (ARDS) incorporating comprehensive ventilatory, acute physiological, organ dysfunction, oxygenation, and nutritional parameters. This study aims to determine the risk factors of ARDS mortality from the above-mentioned parameters at 48 h of invasive mechanical ventilation (IMV), which are feasible across most intensive care unit settings.
Methods: Prospective, observational, single-center study with 150 patients with ARDS defined by Berlin definition, receiving IMV with lung protective strategy.
Results: Our study had a mortality of 41.3% (62/150). We developed a 9-point novel prediction score, the driving pressure oxygenation and nutritional evaluation (DRONE) score comprising of driving pressure (DP), oxygenation accessed by the ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO/FiO) ratio and nutritional evaluation using the modified nutrition risk in the critically ill (mNUTRIC) score. Each component of the DRONE score with the cutoff value to predict mortality was assigned a particular score (the lowest DP within 48 h in a patient being always ≥15 cmHO a score of 2, the highest achievable PaO/FiO <208 was assigned a score of 4 and the mNUTRIC score ≥4 was assigned a score of (3). We obtained the DRONE score ≥4, area under the curve 0.860 to predict mortality. Cox regression for the DRONE score >4 was highly associated with mortality ( < 0.001, hazard ratio 5.43, 95% confidence interval [2.94-10.047]). Internal validation was done by bootstrap analysis. The clinical utility of the DRONE score ≥4 was assessed by Kaplan-Meier curve which showed significance.
Conclusions: The DRONE score ≥4 could be a reliable predictor of mortality at 48 h in ARDS patients receiving IMV.
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http://dx.doi.org/10.4103/jets.jets_12_23 | DOI Listing |
Plant Methods
December 2024
Department of Environmental System Sciences, Institute of Integrative Biology, ETH, Zurich, Switzerland.
Background: Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing.
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December 2024
Faculty of Electrical and Electronic Engineering, University of Transport and Communications, Hanoi 100000, Vietnam.
The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not.
View Article and Find Full Text PDFBioinspir Biomim
January 2025
Department of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Nakhon Pathom 73170, Thailand.
This study focuses on improving coordination among teams of heterogeneous robots, including unmanned aerial vehicles and unmanned ground vehicles, drawing inspiration from natural pack-hunting strategies. The goal is to increase the effectiveness of rescue operations using a new framework that combines hierarchical decision-making with decentralised control. The approach features dynamic target assignment and real-time task allocation based on a scoring function that considers multiple factors, such as the distance to the target, energy usage, communication ability, and potential for energy exchange.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi, Ethiopia.
A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India.
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