Comparison of mechanical, chemical and physical human models of in vivo skin damage: Randomized controlled trial.

INTRODUCTION: Human in vivo models of skin damage were often used in research of cutaneous disorders. The most commonly used models were tape-stripping as mechanical, sodium lauryl sulphate-induced irritation as chemical and ultraviolet radiation as physical damage model. In regard to differences between models, they were expected to have different responses to damage and recovery, with unique skin parameters' changes over time. OBJECTIVE: The aim was to compare skin parameters in three different skin damage models on the same anatomical location, with and without topical treatment. METHODS: Four test sites on each forearm were randomly assigned to three skin damage models with the fourth sites on each forearm chosen as a control, undamaged site. Skin parameters were assessed using non-invasive methods. RESULTS: Sodium lauryl sulphate irritation caused the strongest damage with delayed reaction to the irritant. Tape stripping leads to highest initial skin barrier disruption but afterwards it showed the fastest skin recovery. Ultraviolet radiation did not affect skin barrier function, but it elevated skin erythema and melanin level. Tested preparation did not lead to changes in measured parameters. CONCLUSION: The skin of the participants had different response to three skin damage models with distinct changes of skin parameters and recovery. The trial was registered at ClinicalTrials.gov under the identifier NCT03783819.

Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia.

The aim of our study was to establish and compare the diagnostic accuracy and clinical applicability of published chest CT severity scoring systems used for COVID-19 pneumonia assessment and to propose the most efficient CT scoring system with the highest diagnostic performance and the most accurate prediction of disease severity. This retrospective study included 218 patients with PCR-confirmed SARS-CoV-2 infection and chest CT. Two radiologists blindly evaluated CT scans and calculated nine different CT severity scores (CT SSs). The diagnostic validity of CT SSs was tested by ROC analysis. Interobserver agreement was excellent (intraclass correlation coefficient: 0.982-0.995). The predominance of either consolidations or a combination of consolidations and ground-glass opacities (GGOs) was a predictor of more severe disease (both p < 0.005), while GGO prevalence alone was not. Correlation between all CT SSs was high, ranging from 0.848 to 0.971. CT SS 30 had the highest diagnostic accuracy (AUC = 0.805) in discriminating mild from severe COVID-19 disease compared to all the other proposed scoring systems (AUC range 0.755-0.788). In conclusion, CT SS 30 achieved the highest diagnostic accuracy in predicting the severity of COVID-19 disease while maintaining simplicity, reproducibility, and applicability in complex clinical settings.