Methacrylate allergy is a common cause of allergic contact dermatitis, and its incidence has surged over the past decade. Consequently, the primary sensitizing agent, 2-hydroxyethyl methacrylate, was recently added to the European Baseline Series of contact allergens. This study aimed to assess the added value of testing for allergens included in the (Meth)Acrylate Series - Nails, in addition to 2-hydroxyethyl methacrylate, as well as to characterize patients who may benefit from more extensive testing. A retrospective analysis of medical records of patch-tested patients was conducted between June 2013 and July 2022. Among the 3,828 patients who underwent patch testing, 396 were tested with the (Meth)Acrylate Series - Nails; 153 (38.6%) of those patients tested positive for at least 1 acrylate. The most common hapten was 2-hydroxyethyl methacrylate (85.6%), followed by hydroxypropyl methacrylate (85.0%) and ethylene glycol dimethacrylate (80.4%). In our study, 22/153 patients (14.4%) would have been missed if tested only for 2-hydroxyethyl methacrylate. The analysis showed that including hydroxypropyl methacrylate and ethylene glycol dimethacrylate improved detection rate to 98%, rendering the use of the entire tray unnecessary in most cases.

Pediatric tinea capitis displays a wide range of prevalence, with significant variability among populations. We retrospectively extracted the medical records of 456 pediatric patients diagnosed with tinea capitis during the years 2010–2021, from the dermatology outpatient clinics in two tertiary medical centers. Three species were isolated in 90% of patients: T. tonsurans, M. canis, and T. violaceum. While T. tonsurans presented a six-fold increase in incidence during the years 2019–2021, M. canis maintained stable incidence rates. Furthermore, terbinafine was the most efficient antifungal agent against T. tonsurans, achieving complete clinical clearance in 95% of patients, as compared to fluconazole (68%) and griseofulvin (38%) (p < 0.001). The mycological cure was recorded in 61/90 (68%) of patients with available data, at an average of 10 weeks. For patients with M. canis, griseofulvin and fluconazole were equally efficient (73% and 66%, respectively) (p = 0.44). Kerion was described in 36% and 14% of patients with T. tonsurans and M. canis, respectively, (p < 0.001). In conclusion, since 2019, there has been a significant increase in the prevalence of T. tonsurans, establishing this pathogen as the most common cause for tinea capitis in our population. Our data suggest that terbinafine is effective and presents high cure rates for tinea capitis in the pediatric population.

Background: Emerging evidence indicates that several hematological markers can be used to evaluate treatment response, prediction, and early relapse detection in different inflammatory conditions. This study aimed to investigate the correlation between the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, platelet-to-neutrophil ratio, mean platelet volume, and disease activity in patients with pemphigus vulgaris. Methods: Fifty-six patients (20 men, 36 women; mean age 54 ± 14 years) diagnosed with pemphigus vulgaris were included in this retrospective study. Patients were divided into those treated and not treated with rituximab (groups 1 and 2), and into those who did and did not develop relapse (groups 3 and 4). The neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, platelet-to-neutrophil ratio and mean platelet volume were evaluated at the time of diagnosis, remission, and relapse. The relationship between each marker and disease stage was analyzed using the Wilcoxon rank-sum test for pairwise comparisons. Results: The neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio showed a positive correlation with disease activity, while the platelet-to-neutrophil ratio and mean platelet volume showed a negative correlation. The neutrophil-to-lymphocyte ratio significantly decreased in remission (p < 0.001) and significantly increased in relapse (p < 0.01). The platelet-to-lymphocyte ratio significantly decreased in remission (p < 0.001) and showed no significant change in relapse. The platelet-to-neutrophil ratio significantly increased in remission (p < 0.001) and significantly decreased at relapse (p < 0.001). The mean platelet volume significantly increased in remission (p < 0.001) and decreased non-significantly at relapse. A more significant decrease in the neutrophil-to-lymphocyte ratio in remission was found in patients not treated with rituximab. No significant differences were observed between patients who developed relapse and those who did not. Conclusion: Our results suggest that the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, platelet-to-neutrophil ratio, and mean platelet volume can be useful markers for monitoring treatment response, while the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio can also assist in detecting early relapse.

Background: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity. Methods: Two parallel studies were conducted: a laboratory retrospective study (LABS, n = 482 biopsies) and a non-interventional prospective observational study (OBS, n = 63 biopsies). A training data set of biopsy-verified reports, normal and cancerous skin lesions (n = 3954), were used to develop a DL classifier exploring visual features (System A). The outputs of the classifier were sonified, i.e. data conversion into sound (System B). Derived sound files were analyzed by a second machine learning classifier, either as raw audio (LABS, OBS) or following conversion into spectrograms (LABS) and by image analysis and human heuristics (OBS). The OBS criteria outcomes were System A specificity and System B sensitivity as raw sounds, spectrogram areas or heuristics. Findings: LABS employed dermoscopies, half benign half malignant, and compared the accuracy of Systems A and B. System A algorithm resulted in a ROC AUC of 0.976 (95% CI, 0.965-0.987). Secondary machine learning analysis of raw sound, FFT and Spectrogram ROC curves resulted in AUC's of 0.931 (95% CI 0.881-0.981), 0.90 (95% CI 0.838-0.963) and 0.988 (CI 95% 0.973-1.001), respectively. OBS analysis of raw sound dermoscopies by the secondary machine learning resulted in a ROC AUC of 0.819 (95% CI, 0.7956 to 0.8406). OBS image analysis of AUC for spectrograms displayed a ROC AUC of 0.808 (CI 95% 0.6945 To 0.9208). By applying a heuristic analysis of Systems A and B a sensitivity of 86% and specificity of 91% were derived in the clinical study. Interpretation: Adding a second stage of processing, which includes a deep learning algorithm of sonification and heuristic inspection with machine learning, significantly improves diagnostic accuracy. A combined two-stage system is expected to assist clinical decisions and de-escalate the current trend of over-diagnosis of skin cancer lesions as pathological. (C) 2019 The Author(s). Published by Elsevier B.V.