Characterisation of superficial corneocytes in skin areas of the face exposed to prolonged usage of respirators by healthcare professionals during COVID-19 pandemic.

INTRODUCTION: During the COVID-19 pandemic healthcare workers (HCWs) have used respiratory protective equipment for prolonged periods, which has been associated with detrimental effects on the underlying skin. The present study aims to evaluate changes in the main cells (corneocytes) of the stratum corneum (SC) following prolonged and consecutive use of respirators. METHODS: 17 HCWs who wore respirators daily during routine hospital practice were recruited to a longitudinal cohort study. Corneocytes were collected via tape stripping from a negative control site (area outside the respirator) and from the cheek which was in contact with the device. Corneocytes were sampled on three occasions and analysed for the level of positive-involucrin cornified envelopes (CEs) and the amount of desmoglein-1 (Dsg1), as indirect measurements of immature CEs and corneodesmosomes (CDs), respectively. These were compared to biophysical measurements (Transepidermal water loss, TEWL, and SC hydration) at the same investigation sites. RESULTS: A large degree of inter-subject variability was observed, with maximum coefficients of variation of 43% and 30% for the level of immature CEs and Dsg1, respectively. Although it was observed that there was not an effect of prolonged respirator usage on the properties of corneocytes, the level of CDs was greater at the cheek than the negative control site (p < 0.05). Furthermore, low levels of immature CEs correlated with greater TEWL values after prolonged respirator application (p < 0.01). It was also noted that a smaller proportion of immature CEs and CDs was associated with a reduced incidence of self-reported skin adverse reactions (p < 0.001). CONCLUSIONS: This is the first study that investigated changes in corneocyte properties in the context of prolonged mechanical loading following respirator application. Although differences were not recorded over time, the levels of CDs and immature CEs were consistently higher in the loaded cheek compared to the negative control site and were positively correlated with a greater number of self-reported skin adverse reactions. Further studies are required to evaluate the role of corneocyte characteristics in the evaluation of both healthy and damaged skin sites.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.