Rituximab is a beneficial and relatively safe adjuvant treatment for PV that facilitates prolonged clinical remission and has a significant steroid-sparing effect.
Background: Chanarin Dorfman Syndrome (CDS) is a rare autosomal recessive disorder characterized by the multisytemic accumulation of neutral lipids inside the cytoplasmic lipid droplets. This condition is caused by mutations in the abhydrolase domain containing 5 gene (ABHD5). In CDS the skin involvement is the prevalent and always observed clinical feature, consisting of a non-bullous congenital ichthyosiform erythroderma (NCIE). Moreover, a variable involvement of the liver and neuromuscular system can be also observed. In this report, we aimed to perform the clinical and genetic characterization of a patient affected by CDS with atypical dermatological findings, considering this rare inborn error of neutral lipid metabolism. Methods: Genomic DNA samples obtained from patient and his parents were used to perform the sequencing of the ABHD5 exons and their intron/exon boundaries. Bioinformatic analyses were performed to investigate the possible effect of the identified mutation on protein structure. Results: Here we present the case of a 29-year-old male patient with CDS, who, for long time, has been misdiagnosed as pityriasis rubra pilaris (PRP). He has a history of increasing hyperlipidemia; hepatomegaly associated with hepatosteatosis was also detected. ABHD5 molecular analysis revealed a novel missense mutation, the c.811G > A (p.G271R). Bioinformatic investigations showed that the variant has a deleterious effect on ABHD5 function, probably causing an incorrect folding of the mutant protein. Conclusions: These results highlihts the importance of genetic testing for ABHD5 in unresolved cases of patients presenting unusual skin lesions, that resemble PRP, associated with a history of hyperlipidemia and nonalcoholic fatty liver.
Tzanck smear test is a low-cost, rapid and reliable tool which can be used for the diagnosis of many erosive-vesiculobullous, tumoral and granulomatous diseases. Currently its use is limited mainly due to lack of experience in interpretation of the smears. We developed a deep learning model, TzanckNet, that can identify cells in Tzanck smear test findings. TzanckNet was trained on a retrospective development dataset of 2260 Tzanck smear images collected between December 2006 and December 2019. The finalized model was evaluated using a prospective validation dataset of 359 Tzanck smear images collected from 15 patients during January 2020. It is designed to recognize six cell types (acantholytic cells, eosinophils, hypha, multinucleated giant cells, normal keratinocytes and tadpole cells). For 359 images and 6 cell types, TzanckNet made 2154 predictions. The accuracy was 94.3% (95% CI 93.4–95.3), the sensitivity was 83.7% (95% CI 80.3–87.0) and the specificity was 97.3% (95% CI 96.5–98.1). The area under the receiver operating characteristic curve was 0.974. Our results show that TzanckNet has the potential to lower the experience barrier needed to use this test, broadening its user base, and hence improving patient well-being.
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