Segmentation on Chest CT Imaging in COVID- 19 Based on the Improvement Attention U-Net Model

This paper proposes a new deep learning model to detect COVID-19 lesions in chest CT images. This method is based on the Attention U-net which uses the layer of Atrous Spatial Pyramid Pooling (ASPP) to capture the feature on various scales. It also contains an attention gate. The attention gate provides the ability to suppress irrelevant regions and focus on the useful feature in an input image. The experimental results show that this method can achieve 99.61% accuracy and 80.43% precision. They are more effectively than the baseline method on Chest CT images. © 2022 The authors and IOS Press. All rights reserved.

Resilience to withstand covid-19 crisis: Lessons from a foreign language institution in vietnam

The rapid switch to online teaching due to COVID-19 pandemic has caused major setbacks in the education sector worldwide. This paper explored the responses of a Foreign Language University in Vietnam amid this transition from a holistic approach, moving from the institution’s policies to realising such policies via administrative, supporting, and teaching staff’s implementation. It further examined high-experienced teachers’ use of technologies in their online teaching. Using semi-structured interviews with two administrative and supporting staff members and five English Foreign Language (EFL) lecturers, the study revealed critical themes in administrative and EFL teaching aspects. Regarding administration, the institution developed coherent policies, established the Response Team, and utilised ICT sustainably in their education system. The teaching practice includes thoughtful lesson design, constant support to students, and proactive coping with challenges. Initiatives taken at the institution and individual levels are also discussed. This study would provide practical and relevant lessons to global practitioners and to online education development in EFL programmes during and after COVID-19 pandemic. © 2021, CALL-EJ. All rights reserved.

Aerosolisation during tracheal intubation and extubation in an operating theatre setting.

Aerosol-generating procedures such as tracheal intubation and extubation pose a potential risk to healthcare workers because of the possibility of airborne transmission of infection. Detailed characterisation of aerosol quantities, particle size and generating activities has been undertaken in a number of simulations but not in actual clinical practice. The aim of this study was to determine whether the processes of facemask ventilation, tracheal intubation and extubation generate aerosols in clinical practice, and to characterise any aerosols produced. In this observational study, patients scheduled to undergo elective endonasal pituitary surgery without symptoms of COVID-19 were recruited. Airway management including tracheal intubation and extubation was performed in a standard positive pressure operating room with aerosols detected using laser-based particle image velocimetry to detect larger particles, and spectrometry with continuous air sampling to detect smaller particles. A total of 482,960 data points were assessed for complete procedures in three patients. Facemask ventilation, tracheal tube insertion and cuff inflation generated small particles 30-300 times above background noise that remained suspended in airflows and spread from the patient's facial region throughout the confines of the operating theatre. Safe clinical practice of these procedures should reflect these particle profiles. This adds to data that inform decisions regarding the appropriate precautions to take in a real-world setting.