Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.

This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks.

Health-related quality of life following surgical attenuation of congenital portosystemic shunts versus healthy controls.

Objectives: To design a health-related quality of life questionnaire for dogs with congenital portosystemic shunts, use it in a cohort of dogs treated with suture attenuation and compare results with those obtained from a healthy control cohort.

Materials And Methods: Data were collected from the hospital records of dogs treated with suture ligation of an intrahepatic or extrahepatic congenital portosystemic shunt at two referral centres. Owners were asked to complete a questionnaire assessing their dog's health-related quality of life preoperatively (retrospectively) and at the time of follow-up.

Long-term serum bile acid concentrations in 51 dogs after complete extrahepatic congenital portosystemic shunt ligation.

Objectives: To report the long-term bile acid stimulation test results for dogs that have undergone complete suture ligation of a single congenital extrahepatic portosystemic shunt.

Materials And Methods: Data were collected from the hospital records of all dogs that had undergone a complete suture ligation of a single congenital extrahepatic portosystemic shunt. Owners were invited to return to the referral centre or their local veterinarian for repeat serum bile acid measurement.

Results of a transparent expert consultation on patient and public involvement in palliative care research.

Background: Support and evidence for patient, unpaid caregiver and public involvement in research (user involvement) are growing. Consensus on how best to involve users in palliative care research is lacking.

Aim: To determine an optimal user-involvement model for palliative care research.