Monkeypox infection is a zoonosis first described in humans in 1970 in Congo. While previously manifesting in small, confined outbreaks, the disease is rapidly spreading globally.

Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) have gained prominence for their efficacy in treating type 2 diabetes and obesity. Recent evidence suggests that their pleiotropic effects—beyond glycemic control and weight loss—include anti-inflammatory, immunomodulatory, and antioxidative effects, which may beneficially support various dermatologic conditions such as psoriasis, hidradenitis suppurativa, acanthosis nigricans, and Hailey-Hailey disease. However, GLP-1 RAs are also associated with emerging cutaneous adverse drug reactions, including bullous, exanthematous and vasculitic manifestations, and other rare side effects. In aesthetic and reconstructive surgery, rapid weight loss induced by these agents has raised concerns regarding facial volume depletion, skin laxity, and impaired wound healing. In addition, perioperative management of patients on GLP-1 RAs requires careful assessment as a result of delayed gastric emptying and the associated potential risk of pulmonary aspiration. This narrative review summarizes current knowledge on the benefit/risk profile of GLP-1 RAs, highlighting their impact for dermatologists and plastic surgeons in relevant contexts.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

Skin cancers involve a significant concern in cancer therapy due to their association with various treatment modalities. This comprehensive review explores the increased risk of skin cancers linked to different anti-cancer treatments, including classic immunosuppressants such as methotrexate (MTX), chemotherapeutic agents such as fludarabine and hydroxyurea (HU), targeted therapies like ibrutinib and Janus Kinase inhibitors (JAKi), mitogen-activated protein kinase pathway (MAPKP) inhibitors, sonic hedgehog pathway (SHHP) inhibitors, and radiotherapy. MTX, a widely used immunosuppressant in different fields, is associated with basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and cutaneous melanoma (CM), particularly at higher dosages. Fludarabine, HU, and other chemotherapeutic agents increase the risk of non-melanoma skin cancers (NMSCs), including cSCC and BCC. Targeted therapies like ibrutinib and JAKi have been linked to an elevated incidence of NMSCs and CM. MAPKP inhibitors, particularly BRAF inhibitors like vemurafenib, are associated with the development of cSCCs and second primary melanomas (SPMs). SHHP inhibitors like vismodegib have been linked to the emergence of cSCCs following treatment for BCC. Additionally, radiotherapy carries carcinogenic risks, especially for BCCs, with increased risks, especially with younger age at the moment of exposure. Understanding these risks and implementing appropriate screening is crucial for effectively managing patients undergoing anti-cancer therapies.