The heterogeneous data generated within IoMT environments have presented significant challenges in ML-based attack detection approaches, where the lack of standardized features creates a barrier. Current ML-based attack detection methods rely on feature extraction techniques, often requiring specialized security expertise to analyze and identify the most relevant features for modeling ML algorithms, hindering widespread adoption in IoMT. This study presents a new approach for detecting ransomware-spreading behavior based on Nilsimsa fingerprinting and Machine Learning to represent network traffic and detect infected network flows. The performance of our proposal was evaluated using two IoMT datasets, ICE and CICIoMT2024. Our approach demonstrated better performance than current ML-based attack detection methods using network traffic features in terms of precision, F1-score, and training efficiency across both datasets. The Random Forest algorithm modeled with Nilsimsa fingerprints on the ICE dataset achieved 100% precision and 98.72% F1-score. Similarly, on the CICIoMT2024 dataset, our approach exhibited 99.44% precision and 98.59% F1-score.

Agminated nevus refers to the presence of multiple nevi grouped in a circumscribed skin area; it is rarely reported in the literature. This report presents the case of a 10-year-old female patient with a history of Langerhans cell histiocytosis, who presents multiple nevi in the lumbar and inguinal region. In the histopathological study, an atypical melanocytic nevus was reported. Wood’s lamp examination discarded the presence of nevus spilus, and the diagnosis of agminated nevus was reached. The association of this type of nevus with Langerhans cell histiocytosis is rare, and only four cases were found reported in the indexed literature. The reason for this association is unknown, thus a new theory about its origin is presented here.

Background: Some cytokines are strongly implicated in the development of squamous cell carcinoma (SCC) such as the Macrophage migration inhibitory factor (MIF). The haplotype ‐794 (CATT)_5–8/‐173G>C in MIF gene polymorphisms has been associated with some types of cancer. The aim of this study is to establish the possible association between the presence of this haplotype in the MIF gene and its subsequent soluble levels with the susceptibility of SCC in western Mexican population.Methods: This study included 175 SCC patients and 175 age–sex‐matched individuals as a reference group (RG) from western Mexico. Genomic DNA was extracted from peripheral blood leukocytes. Polymorphisms were genotyped by endpoint PCR and PCR‐RFLP, and the determination of MIF serum levels was measured by ELISA. Clinical characteristics were evaluated by a group of dermatologists.Results: Analysis of [‐794(CATT)_5–8/‐173G>C] MIF gene polymorphisms showed that the 5C (OR = 2.7, p = 0.02) and the 7G (OR = 3.39, p < 0.01) haplotypes are associated with susceptibility in SCC. MIF soluble levels in SCC patients showed a median of 13.93 ng/mL, whereas the reference group showed 6.000 ng/mL.Conclusions: Our findings suggest that 5C and 7G [‐794(CATT)_5–8/‐173G>C] MIF gene haplotypes are associated with susceptibility to SCC and that SCC patients present increased soluble levels of MIF.