Perceptions and attitudes of dental practitioners towards impacts of Covid 19 pandemic on clinical dentistry: a cross-sectional study.

BACKGROUND: The COVID-19 pandemic challenged all healthcare providers including dental practitioners. This cross-sectional study aimed to investigate the dental practitioners' perceptions and attitudes towards the impacts of COVID-19 on their professional practice, career decision and patient care. METHODS: Data was collected from dental practitioners registered in New South Wales (NSW), Australia using an online survey. RESULTS AND CONCLUSION: Responses received from 206 dental practitioners revealed their perceptions and attitudes towards COVID-19 infection risk, clinical guidelines, and measures adopted to deliver patient care. Majority of participants perceived the risk of infection in dentistry was higher compared with other health professionals. Most dental practices have followed guidelines received from professional associations and adopted multiple measures such as providing hand sanitizer, social distancing, and risk screen, to ensure safe delivery of oral health care. Over 80% of dental practitioners raised concerns on patients' accessibility to dental care during the pandemic. Despite tele-dentistry was introduced, almost half of the participants did not recognize tele-dentistry as an effective alternative. Moreover, negative impacts of COVID-19 pandemic on dental practitioner's professional career have been reported, including lower practice safety, reduction in working hours and income. Noteworthy, one quarter of participants even considered changing their practice environment, moving sectors or even leaving their career in dentistry. However, majority of the dental practitioners are willing to stay in their current practice environment and continue their career in dentistry. Our observations demonstrate the systematic disruption to dental practice faced in Australia due to the COVID-19 pandemic. Providing dental practitioners with timely educational training and support is important to minimise negative impacts of the challenges and to optimise dental care.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.