Dermato, Volume 4, Issue 4 (December 2024) – 6 articles

Background/Objectives: A prior placebo-controlled, double-blind clinical study demonstrated the superiority of a topical combination of heparin and levomenol over the control and single active constituents. The effect on pruritus and the total SCORAD index were used to evaluate efficacy, but not the effect on the individual SCORAD items. This analysis investigates the overall efficacy of the treatment and the relative contributions of heparin and levomenol to symptom relief in atopic dermatitis, including the effects on the affected body area and its implications for sleeplessness. Methods: The ITT group (combination group A: n = 79; levomenol group B: n = 80; heparin group C: n = 78; placebo group D: n = 41) of the previously published study was re-analysed. Results: The combination significantly improved symptoms such as erythema, edema/papulation, excoriations, and skin dryness compared to the control after eight weeks of treatment. No significant differences were observed for oozing/crust and lichenification among the active groups. Levomenol and heparin exhibited significant advantages over the control for erythema and excoriations, while heparin improved edema/papulation significantly. The inflamed body area significantly decreased using the combination or single active constituents, and sleeplessness significantly improved with the combination or with heparin alone compared to the control. Conclusions: These findings highlight the efficacy of the combination and individual active constituents in addressing specific symptoms of atopic dermatitis, providing insights into their therapeutic effects. The combination’s advantage over the control is most evident in improving excoriations, skin dryness, inflamed skin area, and sleeplessness, justifying its documented use as an intervention in the early stages of atopic dermatitis episodes. Full article

Background/Objectives: IgA vasculitis, formerly Henoch-Schönlein purpura (HSP), is a form of systemic vasculitis that most commonly affects children. We present a case of hard-to-heal lower extremity lesions in a patient with confirmed IgA vasculitis or HSP. Methods: The patient was serially imaged with a handheld, bacterial autofluorescence imaging device across subsequent visits. Results: Due to the identification of bacterial burden presence that was not clinically evident, the patient was prescribed antibiotics and promptly healed. Conclusions: This case illustrates the value of bacterial fluorescence imaging in dermatological cases. Full article

Background: Narrowband ultraviolet B (nb-UVB) is one of the most popular and effective modalities to treat vitiligo. Given the importance of nb-UBV, as well as its associations, this research searched the literature for answers on how to best treat vitiligo. Objective: To conduct a systematic literature review assessing the efficacy of narrowband ultraviolet B (NB-UVB) therapy for the treatment of vitiligo, in comparison with psoralen plus ultraviolet A (PUVA) therapy and other topical or systemic treatment modalities. Methods: The databases included were PubMed, Embase, Cochrane, Scopus, Web of Science, and LILACS. Gray literature was also used: Google Scholar, Open Grey, and Library of Thesis and Dissertations-CAPES. The search used the keywords: “Vitiligo” AND “Ultraviolet Therapy OR Actinotherapy”. The risk of bias was evaluated using the Cochrane Risk of Bias 2 (RoB 2) tool by two independent, blinded reviewers, with disagreements resolved by a third reviewer. The outcome assessed was repigmentation. Results: Three randomized controlled trials were selected from 2973 records. In three studies, Nb-UVB had superior effects to the comparator. Conclusion: Nb-UVB phototherapy improves repigmentation in patients with vitiligo, with few side effects. However, the variability in the protocols and the risk of bias require caution when interpreting the results. Full article

With the growing complexity of skin disorders and the challenges of traditional diagnostic methods, AI offers exciting new solutions that can enhance the accuracy and efficiency of dermatological assessments. Reflectance Confocal Microscopy (RCM) stands out as a non-invasive imaging technique that delivers detailed views of the skin at the cellular level, proving its immense value in dermatology. The manual analysis of RCM images, however, tends to be slow and inconsistent. By combining artificial intelligence (AI) with RCM, this approach introduces a transformative shift toward precise, data-driven dermatopathology, supporting more accurate patient stratification, tailored treatments, and enhanced dermatological care. Advancements in AI are set to revolutionize this process. This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. The discussion highlights AI’s role in identifying and classifying skin conditions, offering benefits such as a greater consistency and a reduced strain on healthcare professionals. Furthermore, the paper explores AI integration into dermatological practices, addressing current challenges and future possibilities. The synergy between AI and RCM holds the potential to significantly advance skin disease diagnosis, ultimately leading to better therapeutic personalization and comprehensive dermatological care. Full article

Background: Lichen sclerosus (LS) is a chronic, inflammatory dermatosis that affects both genital and extragenital sites. It is often difficult to treat and may lead to a variety of complications if not adequately treated. The mainstay of therapy involves topical corticosteroids, topical calcineurin inhibitors, and systemic immunomodulators. Although a variety of topical, oral, and procedural therapies are available, a review comparing relative efficacy is lacking. To this end, this systematic review aimed to summarize the literature regarding treatment modalities and their respective response rates in patients with genital LS. Methods: A literature search was conducted in accordance with PRISMA guidelines. Results: This review qualitatively summarizes information from 31 randomized controlled trials, encapsulating a total of 1507 patients with LS, the majority of which were female (n = 1374, 91%). Topical corticosteroids, the mainstay of therapy for LS, were discussed throughout the literature, and proved to be more efficient than topical calcineurin inhibitors, topical hormonal therapy, topical vitamin E oil and cold cream. However, other treatment modalities proved to be more efficient than topical corticosteroids, including CO₂ and Nd:YAG laser therapies, and the addition of polydeoxyribonucleotide intradermal injections, to steroid therapy. Finally, other modalities that proved to be efficient in the treatment of LS included silk undergarments, human fibroblast lysate cream, platelet-rich plasma, acitretin, and surgical intervention. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials. Limitations included the inclusion of only randomized controlled trials, moderate or high risk of bias, and heterogeneity in treatment regimens, among others. Conclusion: Although high-potency topical corticosteroids have validated efficacy in the management of LS, other treatment modalities, including steroid-sparing agents and/or procedural adjuncts, have been demonstrated to have a beneficial role in the treatment of LS. Full article

Large language models (LLMs) are trained using large datasets and may be applied to language-based tasks. Studies have demonstrated their ability to perform and pass postgraduate medical examinations, and with the increasingly sophisticated deep learning algorithms and incorporation of image-analysis capabilities, they may also be applied to the Specialty Certificate Examination (SCE) in Dermatology. The Dermatology SCE sample questions were used to assess the performance of five freely available and high-performance LLMs. The LLMs’ performances were recorded by comparing their output on multiple-choice questions against the sample answers. One hundred questions, four of which included photographs, were entered into the LLMs. The responses were recorded and analysed, with the pass mark set at 77%. The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). The LLMs were generally capable of interpreting and providing reasoned responses to clinical scenarios and clinical data. This continues to demonstrate the potential of LLMs in both medical education and clinical settings. Full article