RESUMO
Urea is a hygroscopic molecule (capable of absorbing water) present in the epidermis as a component of the natural moisturizing factor (NMF) and is essential for the adequate hydration and integrity of the stratum corneum. Urea improves skin barrier function including antimicrobial defense by regulating gene expression in keratinocytes relevant for their differentiation and antimicrobial peptide production. It also plays a fundamental role in regulating keratinocyte proliferation. One of the first uses of urea in modern medicine was the topical treatment of wounds due to its proteolytic and antibacterial properties. At present, urea is widely used in dermatology to improve skin barrier function and as one of the most common moisturizers and keratolytic agents. Urea-containing formulations are available in diverse formulations and concentrations. Multiple clinical trials on the use of urea-containing formulations have shown significant clinical improvement in many of the dermatosis presenting with scaly and dry skin such as atopic dermatitis, ichthyosis, xerosis, seborrheic dermatitis and psoriasis, among others. Furthermore, urea can increase skin penetration and optimize the action of topical drugs. Urea-based products are well tolerated; their side effects are mild and are more frequent at high concentration. Here, we present a review of the use of urea in dermatology, discussing its mechanism of action, safety profile and frequent indications.
RESUMO
Total body photography is used for early detection of malignant melanoma, primarily as a means of temporal skin surface monitoring. In a prior work, we presented a scanner with a set of algorithms to map and detect changes in pigmented skin lesions, thus demonstrating that it is possible to fully automate the process of total body image acquisition and processing. The key procedure in these algorithms is skin lesion matching that determines whether two images depict the same real lesion. In this paper, we aim to improve it with respect to false positive and negative outcomes. To this end, we developed two novel methods: one based on successive rigid transformations of three-dimensional point clouds and one based on nonrigid coordinate plane deformations in regions of interest around the lesions. In both approaches, we applied a robust outlier rejection procedure based on progressive graph matching. Using the images obtained from the scanner, we created a ground truth dataset tailored to diversify false positive match scenarios. The algorithms were evaluated according to their precision and recall values, and the results demonstrated the superiority of the second approach in all the tests. In the complete interpositional matching experiment, it reached a precision and recall as high as 99.92% and 81.65%, respectively, showing a significant improvement over our original method.
