Effects of different delivery modes on teaching biomedical science practical skills in higher education during the 2021 pandemic measures.

The COVID-19 pandemic related measures had augmented the rise of online education. While online teaching had mitigated the negative impacts from educational institutional closures, it was unable to displace hands-on biomedical laboratory practical lessons effectively. Without practical sessions, there was concern over the imparting of laboratory skills even with video demonstrations. To investigate the effectiveness of different delivery modes in imparting laboratory skills, theoretical and practical student assessments were analyzed alongside an anonymous survey on their motivation and prior experience. The undergraduate students were exposed to (1) instructor-live demonstration; (2) video demonstration or (3) no demonstration prior to the practical test which was a plasmid extraction. Significantly higher mini-prep yields and purity were found for both instructor-live and video demonstrations compared to no demonstration. Comparison with pre-pandemic theoretical assessment performance showed no significant differences despite longer contact hours during pre-pandemic times. Prior lab experience and motivation for selecting the course did not significantly affect student mini-prep yields. In conclusion, our findings suggest that video demonstrations were as effective as instructor-live demonstrations during the pandemic without noticeably compromising the teaching and learning of biomedical laboratory skills.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals

The number of Major Depressive Disorder (MDD) patients is rising rapidly these days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD through personal interviews and by observing electroencephalogram (EEG) signals. Hence, an automated MDD detection system developed using deep learning techniques can help reduce the workload of clinicians by diagnosing MDD accurately. In this study, we have proposed a novel deep learning model based on Convolutional Neural Network (CNN) and spectrogram images. In this work, Short‐Time Fourier Transform (STFT) is first applied to the EEG signals to obtain spectrogram images of MDD patients and healthy subjects. These spectrogram images are then fed to the CNN model for automated detection of MDD patients and healthy subjects. The EEG signals used in this study were obtained from public database with 34 MDD patients and 30 healthy subjects. The highest classification accuracy, precision, sensitivity, specificity, and F1‐score of 99.58%, 99.40%, 99.70%, 99.48%, and 99.55% respectively were obtained with hold‐out validation. Our MDD detection model is highly accurate and needs to be validated with more diverse MDD database before it can be used in clinical settings. Also, we plan to use our developed prototype to detect depression using other physiological signals like electrocardiogram (ECG) and speech signals for accurate and faster diagnosis. [ABSTRACT FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)