COVID-19 Booster Vaccination Adverse Effects and Willingness to Receive a Yearly Booster Dose among Members of Health Sciences Faculties: A Descriptive Cross-Sectional Study.

Background: New severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and waning vaccine efficacy led to the administration of booster doses. Healthcare workers (HCWs) are vulnerable to contract the infection, and vaccination hesitancy in this group may have an impact on vaccine uptake among the general public. Aims: This study aimed to (1) assess the prevalence of self-reported adverse effects (AEs) after the first booster dose vaccine, (2) evaluate the AEs between the homologous and heterologous booster vaccines, and (3) evaluate the willingness to receive a hypothetical yearly booster dose. Materials and Methods: An online, cross-sectional, self-administered, structured questionnaire was distributed to members of the health sciences faculties (HSFs), XXXX University, Malaysia. Convenience sampling was adopted, and descriptive statistics was used to interpret the results. Results: About 67.1% of participants experienced systemic or local AEs. The common AEs were pain at the site of injection (60.2%), fatigue (45.7%), headache (31.6%), and fever (24.7%). About 64% of our participants believed that the booster dose provided extra immunity against the coronavirus disease 2019 (COVID-19) infection and 57.7% of participants expressed concern over the "mix-match" of vaccination. About 78% of the participants were keen to receive the hypothetical yearly booster dose. The severity of AEs between the booster dose and the primary dose was statistically insignificant (P < 0.159). Conclusion: Booster vaccination AEs were similar to the primary dose. However, a higher severity of AEs occurring in heterologous vaccine receivers was noted in our study.

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging.

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.