The Multiscale Surface Vision Transformer.

Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers.

The effect of veno-venous ECMO on the pharmacokinetics of Ritonavir, Darunavir, Tenofovir and Lamivudine.

INTRODUCTION: To our knowledge, there is no published data on the pharmacokinetic (PK) profile of antiretroviral (ART) drugs on patients undergoing extracorporeal membrane oxygenation (ECMO) therapy. We present PK analyses of Ritonavir, Darunavir, Lamivudine and Tenofovir in a patient with HIV who required veno-venous ECMO (VV ECMO). METHODS: Plasma concentrations for Ritonavir, Darunavir, Tenofovir and Lamivudine were obtained while the patient was on ECMO following pre-emptive dose adjustments. Published population PK models were used to simulate plasma concentration profiles for the drugs. The population prediction and the observed plasma concentrations were then overlaid with the expected drug profiles using the individual Bayesian post-hoc parameter estimates. RESULTS: Following dose adjustments, the PK profiles of Ritonavir, Darunavir and Tenofovir fell within the expected range and appeared similar to the population prediction, although slightly different for Ritonavir. The observed data for Lamivudine and its PK profile were completely different from the data available in the literature. CONCLUSIONS: To our knowledge, this is the first study reporting the PK profile of ART drugs during ECMO therapy. Based on our results, dose adjustment of ART drugs while on VV ECMO may be advisable. Further study of the PK profile of Lamivudine is required.