Objectives: To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms.
Materials And Methods: PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles.
Background: The pathophysiology of COVID-19 remains poorly understood. We aimed to estimate the contribution of intrapulmonary shunting and ventilation-to-perfusion (VA/Q) mismatch using a mathematical model to construct oxygen-haemoglobin dissociation curves (ODCs).
Methods: ODCs were constructed using transcutaneous pulse oximetry at two different fractions of inspired oxygen (FiO2).
Acute liver failure is a life-threatening condition commonly caused by drug-induced hepatotoxicity or viral hepatitides. However, there are a number of rarer causes such as haemophagocytic lymphohistiocytosis. Haemophagocytic lymphohistiocytosis is a syndrome of uncontrolled immune cell activation, triggered by infection or malignancy, which carries a high mortality.
View Article and Find Full Text PDFIn this work, we employed wet chemically synthesized bimetallic Au-Ag core-shell nanostructures (Au-AgNSs) to enhance the photocurrent density of mesoporous TiO2 for water splitting and we compared the results with monometallic Au nanoparticles (AuNPs). While Au-AgNSs incorporated photoanodes give rise to 14× enhancement in incident photon to charge carrier efficiency, AuNPs embedded photoanodes result in 6× enhancement. By varying nanoparticle concentration in the photoanodes, we observed ∼245× less Au-AgNSs are required relative to AuNPs to generate similar photocurrent enhancement for solar fuel conversion.
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