The influence of the commensal skin bacterium Staphylococcus epidermidis on the epidermal barrier and inflammation: Implications for atopic dermatitis

The skin microbiota is a crucial component in maintaining cutaneous barrier function. Staphylococcus epidermidis is considered as a beneficial commensal member of the cutaneous microbiota promoting skin health. However, S. epidermidis is also frequently detectable in the skin of patients with the inflammatory skin disease atopic dermatitis (AD) and some studies reported a significantly higher presence of S. epidermidis in severe AD as compared to mild AD. Therefore, this study aimed to analyse the impact of S. epidermidis on the expression of cutaneous inflammatory mediators and skin barrier molecules. Various S. epidermidis skin-derived isolates activated the proinflammatory transcription factor NF-kappaB and induced expression of AD-associated proinflammatory cytokines in human primary keratinocytes and 3D skin equivalents. Skin barrier molecules such as filaggrin were downregulated by S. epidermidis. In general, AD-derived S. epidermidis strains elicited a higher response than strains derived from the skin of healthy individuals. Taken together, our results provide further evidence that the abundance of S. epidermidis in AD may trigger the inflammatory scenario associated with this disease

Staphylococcus aureus Activates the Aryl Hydrocarbon Receptor in Human Keratinocytes

Staphylococcus aureus is an important pathogen causing various infections, including - as most frequently isolated bacterium - cutaneous infections. Keratinocytes as the first barrier cells of the skin respond to S. aureus by the release of defense molecules such as cytokines and antimicrobial peptides. Although several pattern recognition receptors expressed in keratinocytes such as Toll-like and NOD-like receptors have been reported to detect the presence of S. aureus, the mechanisms underlying the interplay between S. aureus and keratinocytes are still emerging. Here, we report that S. aureus induced gene expression of CYP1A1 and CYP1B1, responsive genes of the aryl hydrocarbon receptor (AhR). AhR activation by S. aureus was further confirmed by AhR gene reporter assays. AhR activation was mediated by factor(s) <2 kDa secreted by S. aureus. Whole transcriptome analyses and real-time PCR analyses identified IL-24, IL-6, and IL-1beta as cytokines induced in an AhR-dependent manner in S. aureus-treated keratinocytes. AhR inhibition in a 3D organotypic skin equivalent confirmed the crucial role of the AhR in mediating the induction of IL-24, IL-6, and IL-1beta upon stimulation with living S. aureus. Taken together, we further highlight the important role of the AhR in cutaneous innate defense and identified the AhR as a novel receptor mediating the sensing of the important skin pathogen S. aureus in keratinocytes

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd