Early intubation versus late intubation for COVID-19 patients: An in situ simulation identifying factors affecting performance and infection control in airway management.

COVID-19 poses an infectious risk to healthcare workers especially during airway management. We compared the impact of early versus late intubation on infection control and performance in a randomised in situ simulation, using fluorescent powder as a surrogate for contamination. Twenty anaesthetists and intensivists intubated a simulated patient with COVID-19.

Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT.

Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans.

Methods: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e.

Evaluation of Body-Wise and Organ-Wise Registrations For Abdominal Organs.

Identifying cross-sectional and longitudinal correspondence in the abdomen on computed tomography (CT) scans is necessary for quantitatively tracking change and understanding population characteristics, yet abdominal image registration is a challenging problem. The key difficulty in solving this problem is huge variations in organ dimensions and shapes across subjects. The current standard registration method uses the global or body-wise registration technique, which is based on the global topology for alignment.

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors.

Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models.

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid/gray matter/white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms.

Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors.

Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.

Image registration has become an essential image processing technique to compare data across time and individuals. With the successes in volumetric brain registration, general-purpose software tools are beginning to be applied to abdominal computed tomography (CT) scans. Herein, we evaluate five current tools for registering clinically acquired abdominal CT scans.

Total synthesis of the potent microtubule-stabilizing agent (+)-discodermolide.

The total synthesis of the potent microtubule-stabilizing, antimitotic agent (+)-discodermolide is described. The convergent synthetic strategy takes advantage of the diastereoselective alkylation of a ketone enolate to establish the key C15-C16 bond. The synthesis is amenable to preparation of gram-scale quantities of (+)-discodermolide and analogues.