Objective: Neurally adjusted ventilatory assist (NAVA) is a new ventilator modality with an innovative synchronization technique. Our aim is to verify if NAVA is feasible and safe in terms of physiological and clinical variables in infants recovering from severe acute respiratory distress syndrome (ARDS).
Design: This is a pilot nested study to help future trial design.
Setting: The study was performed in third-level academic pediatric intensive care units.
Patients: Infants affected by severe ARDS requiring high-frequency ventilation and weaned with NAVA during 2010 were included. Controls (2:1 ratio) were ARDS infants weaned with pressure support ventilation (PSV) during 2008-2009 matched for age, gas exchange impairment, and weight.
Main Outcome Measures: The main outcome measures were the physiological and ventilator parameters and the duration of ventilator support in PSV or NAVA.
Results: Ten infants treated with NAVA and 20 with PSV were studied. Heart rate (P < .001) and mean arterial pressure (P < .001) increased less during NAVA than during PSV. Similarly, Pao2/Fio2 ratio decreased less in NAVA than in PSV (P < .001). Neurally adjusted ventilatory assist also resulted in lower Paco2 (P < .001) and peak pressure (P = .001), as well as higher minute ventilation (P = .013). COMFORT score (P = .004) and duration of support were lower in NAVA than in PSV (P = .011).
Conclusions: Neurally adjusted ventilatory assist is safe and suitable in infants recovering from severe ARDS. It could provide better results than PSV and is worth to be investigated in a multicenter randomized trial.
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http://dx.doi.org/10.1016/j.jcrc.2013.08.006 | DOI Listing |
Eur J Pediatr
January 2025
Neonatal Intensive Care Centre, St George's University Hospitals NHS Foundation Trust, London, SW17 0QT, UK.
To assess respiratory changes after neurally adjusted ventilatory assist (NAVA) initiation in preterm infants with evolving or established bronchopulmonary dysplasia (BPD). Premature infants born less than 32 weeks gestation with evolving or established BPD initiated on invasive or non-invasive (NIV) NAVA were included. Respiratory data: PCO and SpO₂/FiO₂ (S/F) ratio before and at 4, 24, 48 h post-NAVA initiation were collected.
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January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
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January 2025
School of Information Science and Technology, Shihezi University, Xinjiang, China.
Predictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educational data is challenging, so the dimensionality of student achievement features needs to be reduced. Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.
View Article and Find Full Text PDFJ R Soc Interface
January 2025
Nantes Université, École Centrale Nantes, IMT Atlantique, CNRS, LS2N, UMR 6004, Nantes F-44000, France.
Dissipative environments are ubiquitous in nature, from microscopic swimmers in low-Reynolds-number fluids to macroscopic animals in frictional media. In this study, we consider a mathematical model of a slender elastic locomotor with an internal rhythmic neural pattern generator to examine various undulatory locomotion such as swimming and crawling behaviours. By using local mechanical load as mechanosensory feedback, we have found that undulatory locomotion robustly emerges in different rheological media.
View Article and Find Full Text PDFDesigning invisibility devices for required frequency bands is important in anti-detection methods in various fields such as communications, construction, and others. However, traditional design methods are time-consuming, with manual adjustment of parameters and continuous trial and error. Fortunately, the data-driven approach based on deep learning has revolutionized the field.
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