Immunohistochemical stains are increasingly used to aid in the diagnosis of malignant melanoma, especially when the differentiation of the tumor is unclear based on examination with hematoxylin and eosin. However, aberrant expression of non-melanocytic markers has been reported in melanomas, which can sometimes be further complicated by the loss of conventional melanocytic markers. This review aims to summarize available data regarding unusual staining patterns in primary and metastatic malignant melanoma. It also raises awareness of the potential pitfalls and highlights the importance of appropriate use and interpretation of broad immunohistochemical markers in the context of clinical and histopathologic findings to facilitate the diagnosis of atypical cases of malignant melanoma.

This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.

Preterm infants require fortification of human milk (HM) with essential fatty acids (FA) to ensure adequate post-natal development. As part of a larger randomized controlled study, we investigated FA metabolism in a subset of 47 clinically stable preterm infants (birth weight ≤1500 g or gestational age ≤32 weeks). Infants were randomized to receive HM supplemented with either a new HM fortifier (nHMF; n = 26) containing 12.5 g medium-chain FA (MCFA), 958 mg linoleic acid (LA), 417 mg α-linolenic acid (ALA), and 157 mg docosahexaenoic acid (DHA) per 100 g of powder (in compliance with the latest guidelines) or a fat-free HMF (cHMF; n = 21). Plasma phospholipid (PL) and triacylglycerol (TAG), and red blood cell phosphatidylcholine (RBC-PC) and phosphatidylethanolamine (RBC-PE) FA profiles were assessed before and after 21 days of feeding. In the nHMF group, significantly increased levels of n-9 monounsaturated fatty acids were observed, formed most likely by elongation and desaturation of dietary saturated fatty acids present in HM. ALA fortification increased ALA assimilation into plasma TAG. Similarly, DHA fortification enriched the DHA content in RBC-PE, which, in this compartment, was not associated with lower arachidonic acid levels as observed in plasma TAG and phospholipids. RBC-PE, a reliable indicator of FA metabolism and accretion, was the most sensitive compartment in this study.