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Results 321-330 of 2278 (Search time: 0.012 seconds).
    • A well-known but rarely seen interaction: propofol with lignocaine
    Authors: Ashwini, Reddy; Sanjay, Kumar; Shalvi, Mahajan;  Advisor: -;  Co-Author: - (2023)

    Propofol is considered an excellent intravenous anesthetic agent. However, a 30–70% incidence of pain associated with its injection is a significant source of patient discontent. Injection pain and discomfort rank as the sixth most crucial perioperative issue (Desousa 2016). Several techniques have been employed to reduce injection discomfort, including the use of the forearm and antecubital veins, freezing or warming the injectate, and aspirating blood before injection. Pre-treatment or contemporaneous administration of thiopentone, pethidine, fentanyl, dexamethasone, nitroglycerine, ketorolac, and local anesthetics has also been considered.
    • SealFSv2 combining storage-based and ratcheting for tamper-evident logging
    Authors: Gorka, Guardiola-Múzquiz; Enrique, Soriano-Salvador;  Advisor: -;  Co-Author: - (2023)

    Traditional local tamper-evident logging systems use cryptographic ratchets. In previous works, we presented SealFS (from now on, SealFSv1), a system that follows a radically different approach for local tamper-evident logging based on keystream storage. In this paper, we present a new version, SealFSv2, which combines ratcheting and storage-based log anti-tamper protection.
    • Pulmonary gas exchange evaluated by machine learning a computer simulation
    Authors: Thomas J., Morgan; Adrian N., Langley; Robin D. C., Barrett;  Advisor: -;  Co-Author: - (2023)

    Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q.
    • A deep learning model for the classification of atrial fibrillation in critically ill patients
    Authors: Brian, Chen; David M., Maslove; Jeffrey D., Curran;  Advisor: -;  Co-Author: - (2023)

    Atrial fibrillation (AF) is the most common cardiac arrhythmia in the intensive care unit and is associated with increased morbidity and mortality. New-onset atrial fibrillation (NOAF) is often initially paroxysmal and fleeting, making it difficult to diagnose, and therefore difficult to understand the true burden of disease. Automated algorithms to detect AF in the ICU have been advocated as a means to better quantify its true burden.