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Background: Preliminary clinical trials of adamgammadex, a new cyclodextrin-based selective reversal agent, have demonstrated its efficacy in reversing neuromuscular block by rocuronium., Methods: This multicentre, randomised, double-blind, positive-controlled, non-inferiority phase III clinical trial compared the efficacy and safety of adamgammadex and sugammadex. We randomised 310 subjects to receive adamgammadex (4 mg kg -1 ) or sugammadex (2 mg kg -1 ) at reappearance of the second twitch of the train-of-four (TOF), and standard safety data were collected., Results: For the primary outcome, the proportion of patients with TOF ratio ≥0.9 within 5 min was 98.7% in the adamgammadex group vs 100% in the sugammadex group, with a point estimate and 95% confidence interval (CI) of 1.3% (-4.6%, +1.3%); the lower limit was greater than the non-inferiority margin of -10%. For the key secondary outcome, the median (inter quartile range) time from the start of administration of adamgammadex or sugammadex to recovery of TOF ratio to 0.9 was 2.25 (1.75, 2.75) min and 1.75 (1.50, 2.00) min, respectively. The difference was 0.50 (95% CI: 0.25, 0.50); the upper limit was lower than the non-inferiority margin of 5 min. In addition, there were no inferior results observed in secondary outcomes. Adamgammadex had a lower incidence of adverse drug reactions compared with sugammadex (anaphylactic reaction, recurarisation, decreased heart rate, and laryngospasm; P=0.047)., Conclusions: Adamgammadex was non-inferior to sugammadex with a possible lower incidence of adverse drug reactions compared with sugammadex. Adamgammadex may have a potential advantage in terms of its overall risk-benefit profile., Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR2000039525. Registered October 30, 2020. https://www.chictr.org.cn/showproj.html?proj=56825., (Copyright © 2023 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Recently, deep learning approaches with various network architectures have drawn significant attention from the magnetic resonance imaging (MRI) community because of their great potential for image reconstruction from undersampled k-space data in fast MRI. However, the robustness of a trained network when applied to test data deviated from training data is still an important open question. In this work, we focus on quantitatively evaluating the influence of image contrast, human anatomy, sampling pattern, undersampling factor, and noise level on the generalization of a trained network composed by a cascade of several CNNs and a data consistency layer, called a deep cascade of convolutional neural network (DC-CNN). The DC-CNN is trained from datasets with different image contrast, human anatomy, sampling pattern, undersampling factor, and noise level, and then applied to test datasets consistent or inconsistent with the training datasets to assess the generalizability of the learned DC-CNN network. The results of our experiments show that reconstruction quality from the DC-CNN network is highly sensitive to sampling pattern, undersampling factor, and noise level, which are closely related to signal-to-noise ratio (SNR), and is relatively less sensitive to the image contrast. We also show that a deviation of human anatomy between training and test data leads to a substantial reduction of image quality for the brain dataset, whereas comparable performance for the chest and knee dataset having fewer anatomy details than brain images. This work further provides some empirical understanding of the generalizability of trained networks when there are deviations between training and test data. It also demonstrates the potential of transfer learning for image reconstruction from datasets different from those used in training the network., (Copyright © 2021 Elsevier Inc. All rights reserved.)