Seroprevalence of COVID-19 and Psychological Distress among Front Liners at the Universiti Malaysia Sabah Campus during the Third Wave of COVID-19.

In 2020, the COVID-19 pandemic struck the globe and disrupted various aspects of psychological wellbeing, more so in frontline workers. Research on assessing the seroprevalence of COVID-19 has been scarce; in addition, there are limited studies assessing the association between the seroprevalence of COVID-19 and psychological distress. Therefore, this study aimed to determine the seroprevalence of COVID-19 and the prevalence of psychological distress and to determine whether sociodemographic variables, occupational information variables, coping styles, and psychological processes might contribute to the development of psychological distress. A cross-sectional study involving 168 Universiti Malaysia Sabah (UMS) front liners was carried out to assess these issues. The Depression, Anxiety and Stress Scale (DASS-21) was employed to assess psychological distress, together with the COVID-19 Rapid Test Kit Antibody (RTK Ab) and a series of questionnaires, including a sociodemographic and occupational information questionnaire, the Balanced Index of Psychological Mindedness (BIPM) questionnaire, the Mindfulness Attention and Awareness Scale (MAAS), the Acceptance and Action Questionnaire (AAQ-II), and the Brief COPE questionnaire. The results demonstrated a seroprevalence of COVID-19 at 8.3% (95% CI = 5.0-14.0). Non-healthcare workers (HCWs) had a higher COVID-19 prevalence. Meanwhile, the prevalence of depression, anxiety, and stress among front liners was low (3.0%, 3.6%, and 1.2%, respectively). Younger people (aged 30 years old or less) and HCWs had a higher prevalence of psychological distress; being a HCW was significantly associated with a higher level of anxiety. Dysfunctional coping and psychological inflexibility were consistently found to be predictors for higher levels of the three psychological distress variables. This study suggested some alternatives that could be explored by mental health providers to address mental health issues among front liners at universities.

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions.

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

Automated "disease/no disease" grading of age-related macular degeneration by an image mining approach.

PURPOSE: To describe and evaluate an automated grading system for age-related macular degeneration (AMD) by color fundus photography. METHODS: An automated "disease/no disease" grading system for AMD was developed based on image-mining techniques. First, image preprocessing was performed to normalize color and nonuniform illumination of the fundus images to define a region of interest and to identify and remove pixels belonging to retinal vessels. To represent images for the prediction task, a graph-based image representation using quadtrees was then adopted. Next, a graph-mining technique was applied to the generated graphs to extract relevant features (in the form of frequent subgraphs) from images of both AMD and healthy volunteers. Features of the training data were then fed into a classifier generator for training purposes before employing the trained classifiers to classify new "unseen" images. RESULTS: The algorithm was evaluated on two publically available fundus-image datasets comprising 258 images (160 AMD and 98 normal). Ten-fold cross validation was used. The experiments produced a best specificity of 100% and a best sensitivity of 99.4% with an overall accuracy of 99.6%. Our approach outperformed previous approaches reported in the literature. CONCLUSIONS: This study has demonstrated a proof-of-concept, image-mining technique for automated AMD grading. This technique has the potential to be further developed as an automated grading tool for future whole-scale AMD screening programs.