Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare vasculitis. A key bio-logical hallmark is peripheral blood eosinophilia while antineutrophil cytoplasmic an-tibody (ANCA) positivity is not always present. Therefore, an early phase suspicion of EGPA leading to a correct diagnosis can be challenging. We performed an observa-tional cross-sectional study limited to baseline data on 38 patients between December 2019 and December 2025 at the Allergy and Clinical Immunology Unit, Messina, Italy. Data collection included eosinophil, neutrophil, and lymphocyte counts; ANCAs and antinuclear antibodies (ANAs); erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) determination; and the Birmingham vasculitis activity score (BVAS) questionnaire: Median eosinophil counts were 685 cells/µL, the neutro-phil-to-lymphocyte ratio (NLR) was 2.29, ESR median values were 24 mm/h, and CRPs were 1.99 mg/L. ANCAs were positive in five patients, and ANAs in 20. The trend in ANA+ patients was of lower median eosinophil counts and higher ESR and CRP me-dian values and median BVASs. General, nervous system, and ENT BVAS domains had higher scores.: Disease activity in EGPA at diagnosis can be difficult to recognize. We defined it as chameleon-like and could not be captured by specific immunological sig-natures. Therefore, an early diagnosis needs multidimensional assessment, integrating laboratory parameters with clinical scores. In our EGPA cohort, ANAs were detected in just over half the patients, suggesting that rather than acting as bystanders, these could help clinicians, together with eosinophil counts, NLRs, and BVASs, to suspect EGPA at an early stage for referral to specialized medical centers.

Allergic anisakiasis (AA), caused by the ingestion of fish contaminated with Anisakis larvae, has emerged as a growing global health concern due to the increasing consumption of raw or undercooked seafood. Anisakis simplex is identified as the primary etiologic species, responsible for gastrointestinal symptoms, IgE-mediated (Type I) or cell-mediated (Type IV) manifestations, and gastro-allergic anisakiasis (GAA), a unique clinical overlap between parasitic infection and acute IgE-mediated food allergy. In this review, we analyzed the epidemiology of Anisakis simplex allergy, the main diagnostic methods to confirm a diagnosis of food allergy, its clinical manifestations, and how these differ in different countries around the world. This multidisciplinary synthesis provides, for the first time, an integrated understanding of Anisakis-induced disease mechanisms across human, animal, and cellular levels. The persistence of allergenic proteins despite standard food processing underscores the need for improved diagnostic tools, public health surveillance, and preventive strategies—particularly in populations with high seafood consumption or occupational exposure. A comprehensive approach combining clinical, molecular, and immunological perspectives is essential to address the expanding global burden of allergic anisakiasis.

Sensitization to non-specific lipid transfer proteins (nsLTPs) is highly prevalent in Mediterranean countries. Pru p 3 from peach is a major allergen responsible for IgE-mediated food allergies. As a panallergen, Pru p 3 shows high sequence homology with nsLTPs from other Rosaceae fruits but also from botanically unrelated sources, including nuts and pollens, leading to extensive cross-reactivity complicating diagnosis and management. Given the worldwide prevalence of peanut and tree nut allergies, this study aimed to investigate sensitization patterns in Pru p 3-sensitized patients with tree nut allergy, using artificial intelligence (AI) to identify predictors of clinical reactivity and severity. Data from Pru p 3–sensitized patients with symptoms to peach and/or nuts were analyzed. Sensitization profiles were modeled using an XGBoost algorithm to explore associations with symptoms and severity. Patients sensitized to Pru p 3 and symptomatic for peach and nuts showed predominant sensitization to peanut and hazelnut, but AI revealed stronger associations between clinical reactivity and sensitization to hazelnut, walnut, and almond. Among patients with nut allergy and peach-asymptomatic, peanut and hazelnut sensitization were most frequent, while peach-symptomatic ones, walnut and almond sensitization predominated. Overall, walnut sensitization emerged as the main predictor of clinical severity and increasing number of sensitizations correlated with higher severity. The XGBoost algorithm identified specific allergen combinations associated with symptoms and severity, highlighting walnut sensitization as the strongest severity predictor. Machine learning approaches represent a promising tool for refining risk stratification and personalizing management in nsLTP-related food allergy.