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Reflectance confocal microscopy is a modern, non-invasive diagnostic method that enables real-time imaging of the epidermis and upper layers of the dermis with nearly histological precision and high contrast. The application of this technology to skin imaging during the last years has resulted in progress of dermatological diagnosis, providing virtual access to living skin, without the need for conventional histopathology. The presented method potentially has broad application in the diagnosis of skin diseases. This article provides a summary of the latest reports and previous achievements in the field of reflectance confocal microscopy. General characteristics of confocal images in selected inflammatory skin diseases are presented.

Increased CSF levels of IFN-γ and IL-17A in syphilitic patients with CSF abnormalities suggest that cells of adaptive immunity (probably T-helper cells producing IFN-γ and IL-17) may contribute to the inflammatory response associated with neurosyphilis. In addition, the lack of correlation between serum and CSF IL-17A levels suggests intrathecal production of this cytokine. Further studies are needed to establish the exact nature of the immune response accompanying neurosyphilis and its clinical significance.

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Reflectance confocal microscopy (RCM) is a modern, non-invasive diagnostic method that enables real-time imaging of epidermis and upper layers of the dermis with a nearly histological precision and high contrast. The application of this technology in skin imaging in the last few years has resulted in the progress of dermatological diagnosis, providing virtual access to the living skin erasing the need for conventional histopathology. The RCM has a potential of wide application in the dermatological diagnostic process with a particular reference to benign and malignant skin tumors. This article provides a summary of the latest reports and previous achievements in the field of RCM application in the diagnostic process of skin neoplasms. A range of dermatological indications and general characteristics of confocal images in various types of tumors are presented.

Mast cells (MCs) are known to be regulators of inflammation and immunity, due to the released mediators and expressed cell surface molecules. Lupus erythematosus (LE) is a group of diseases which can be systemic or limited to the skin. Due to the fact that cytokines and chemokines produced by inflammatory cells contribute to the pathogenesis of LE, we quantified the number of mast cells present in the skin. The aim of the study was to compare the chymase-positive and tryptase-positive mast cell counts within skin biopsies from patients with systemic lupus erythematosus (SLE), discoid lupus erythematosus (DLE) and subacute cutaneous lupus erythematosus (SCLE). The material consisted of 45 skin biopsies: 6 with SLE, 34 with DLE and 5 with SCLE. Chymase-and tryptase-positive cells were stained by immunohistochemistry and counted. The mean count of chymase-positive mast cells was 85.14 hpf for the whole group, 35.83 for SLE, 88.48 for DLE and 121.6 for SCLE. The mean count of tryptase-positive cells was 120.05 hpf for the entire group, 59.17 for SLE, 126.42 for DLE and 149.8 for SCLE. The differences between groups were significant for chymase-and tryptase-positive cells.

Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.

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