Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm.

Objectives: To determine real-life quantitative changes in OCT biomarkers in a large set of treatment naive patients in a real-life setting undergoing anti-VEGF therapy. For this purpose, we devised a novel deep learning based semantic segmentation algorithm providing the first benchmark results for automatic segmentation of 11 OCT features including biomarkers for neovascular age-related macular degeneration (nAMD).

Methods: Training of a Deep U-net based semantic segmentation ensemble algorithm for state-of-the-art semantic segmentation performance which was used to analyze OCT features prior to, after 3 and 12 months of anti-VEGF therapy.

The Dreaded 3-Minute Wait: Does It Really Prevent Operating Room Fires? The IGNITE Trial.

Introduction: Operating room fires can have devastating consequences and as such must be prevented. There exists a paucity of literature requiring further elucidation regarding manufacturer recommendations of a predefined waiting period prior to patient draping after using alcohol-based surgical antiseptics, in order to reduce the risk of operating room fires.

Methods: This was further investigated by exposing two common alcohol-based surgical antiseptics to electrosurgery and open flames at various power settings and time intervals in an ex vivo porcine model.

The effect of a hiding space on the behaviour and heart rate variability of dairy calves during temporary separation from the dam.

In natural settings, newborn calves hide for several days before joining the herd. It is unclear whether dairy calves housed indoors would show similar hiding behaviour. This study aimed to describe the use of an artificial hide provided to calves during temporary separation from the dam and assess the effect it has on lying and sleep-like behaviour, as well as heart rate variability (HRV).

Reconstructing disease dynamics for mechanistic insights and clinical benefit.

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data.