The Experience of Stigma in Nurses on the Early Spread of the COVID-19

BACKGROUND: Nurses are at the forefront of handling the coronavirus disease 2019 (COVID-19) and have a significant risk in handling the disease. The rapid transmission of the virus causes nurses to experience various mental health problems and stigma in performing their duties. AIM: This study explored mental health conditions and the stigma of nurses at the forefront of handling the COVID-19. METHODS: This research was a qualitative study with 17 nurses serving in hospitals and health centers in various provinces in Indonesia. The data analysis of this research employed a descriptive analysis technique. RESULTS: The data analysis revealed four themes: Nurses carrying out their duties as a professional call, psychological and physical responses as a reaction to work stress, stigma due to running a profession, and social support as a reinforcement for carrying out their duties. CONCLUSION: This study concludes that nurses require protection and guarantees for the work risk and the stigma consequences from the community.

Image Processing Framework for Pleural Line (A-Line) Detection in Video Lung Ultrasonography

The implementation of Point-of-care Ultrasound (POCUS) for lung imaging has significant potential in the diagnostic of lung abnormalities through the detection of artifacts in lung ultrasonography (LUS), i.e., pleural line (A-line) and vertical comet-tail artifact (B-line). Detecting the pleural line pattern is an essential feature for further lung diagnosis based on machine learning. A healthy lung correlates with the regular repeated horizontal A-line with a fixed distance between the lines and, ideally, produces a higher intensity. This preliminary work focuses on developing an image processing framework for automatic pleural line (A-line) detection in time series B-mode ultrasound images (video) in a healthy subject as an early stage of further lung image interpretations COVID-19 pneumonia patients. The proposed scheme is based on a top-hat morphological grayscale filter with a texture structure element. An adaptive low pass filter that considers local shape parameter is used to suppress noise and keep the curve line information related to the pleural line. The proposed scheme is evaluated for an open dataset of video LUS. The proposed method can successfully detect the pleural line automatically for typical video LUS acquired using a curved transducer. © 2021 IEEE.