Recurrent necrotizing cellulitis, multi-organ autoimmune disease and humoral immunodeficiency due to a novel NFKB1 frameshift mutation

Background: Mutations in NFKB1(nuclear factor of kappa light polypeptide gene enhancer in B-cells 1) are associated with a variety of clinical symptoms, including lymphadenopathy, splenomegaly, hepatomegaly, autoimmune haemolytic anaemia, arthralgia, recurrent respiratory tract infections and post-operative necrotizing cellulitis. Case presentation: We describe a case of a 47-year-old man, who presented with deep necrotizing cellulitis after incision of a submucous abscess by a dentist. Surgical intervention led to a massive progress. Pyoderma gangraenosum (PG) was diagnosed clinically and confirmed histopathologically. High dose corticosteroids and intravenous immunoglobulins (IVIG) improved wound healing dramatically. Until now, immune mediated inflammation events not only affected the skin, but also multiple inner organs, i.e. the heart, lungs and gut. Sequencing of all coding exons of NFKB1 revealed a heterozygous 1bp deletion in exon 23 predicting a frameshift starting at codon Ala891 and resulting in a subsequent stop codon at position 6 in the new reading frame: NM_003998.4: c.2671del; p.(Ala891Glnfs*6) Acute episodes were always successfully treated with corticosteroids, IVIG and concomitant antibiotics. To prevent further exacerbations, the patient receives IVIG once a month, low-dose corticosteroids and methotrexate. Conclusion: This is the first case of a patient with recurrent necrotizing cellulitis and immune mediated multi organ involvement (heart, lungs, intestine) carrying the novel frameshift mutation c.2671del (p. Ala891Glnfs*6) in NFKB1 effectively treated with IVIG, low-dose corticosteroids and methotrexate

Relevance of biallelic versus monoallelic TNFRSF13B mutations in distinguishing disease-causing from risk-increasing TNFRSF13B variants in antibody deficiency syndromes

TNFRSF13B encodes transmembrane activator and calcium modulator and cyclophilin ligand interactor (TACI), a B cell– specific tumor necrosis factor (TNF) receptor superfamily member. Both biallelic and monoallelic TNFRSF13B mutations were identified in patients with common variable immunodeficiency disorders. The genetic complexity and variable clinical presentation of TACI deficiency prompted us to evaluate the genetic, immunologic, and clinical condition in 50 individuals with TNFRSF13B alterations, following screening of 564 unrelated patients with hypogammaglobulinemia. We identified 13 new sequence variants. The most frequent TNFRSF13B variants (C104R and A181E; n = 39; 6.9%) were also present in a heterozygous state in 2% of 675 controls. All patients with biallelic mutations had hypogammaglobulinemia and nearly all showed impaired binding to a proliferation-inducing ligand (APRIL). However, the majority (n = 41; 82%) of the pa-tients carried monoallelic changes in TNFRSF13B. Presence of a heterozygous mutation was associated with antibody deficiency (P <.001, relative risk 3.6). Heterozygosity for the most common mutation, C104R, was associated with disease (P < .001, relative risk 4.2). Furthermore, heterozygosity for C104R was associated with low numbers of IgD−CD27+ B cells (P = .019), benign lymphoproliferation (P < .001), and autoimmune complications (P = .001). These associations indicate that C104R heterozygosity increases the risk for common variable immunodeficiency disorders and influences clinical presentation

Dual guidance structure for evaluation of patients with unclear diagnosis in centers for rare diseases (ZSE-DUO): study protocol for a controlled multi-center cohort study

Background: In individuals suffering from a rare disease the diagnostic process and the confirmation of a final diagnosis often extends over many years. Factors contributing to delayed diagnosis include health care professionals' limited knowledge of rare diseases and frequent (co-)occurrence of mental disorders that may complicate and delay the diagnostic process. The ZSE-DUO study aims to assess the benefits of a combination of a physician focusing on somatic aspects with a mental health expert working side by side as a tandem in the diagnostic process. Study design: This multi-center, prospective controlled study has a two-phase cohort design. Methods: Two cohorts of 682 patients each are sequentially recruited from 11 university-based German Centers for Rare Diseases (CRD): the standard care cohort (control, somatic expertise only) and the innovative care cohort (experimental, combined somatic and mental health expertise). Individuals aged 12 years and older presenting with symptoms and signs which are not explained by current diagnoses will be included. Data will be collected prior to the first visit to the CRD's outpatient clinic (T0), at the first visit (T1) and 12 months thereafter (T2). Outcomes: Primary outcome is the percentage of patients with one or more confirmed diagnoses covering the symptomatic spectrum presented. Sample size is calculated to detect a 10 percent increase from 30% in standard care to 40% in the innovative dual expert cohort. Secondary outcomes are (a) time to diagnosis/diagnoses explaining the symptomatology; (b) proportion of patients successfully referred from CRD to standard care; (c) costs of diagnosis including incremental cost effectiveness ratios; (d) predictive value of screening instruments administered at T0 to identify patients with mental disorders; (e) patients' quality of life and evaluation of care; and f) physicians' satisfaction with the innovative care approach. Conclusions: This is the first multi-center study to investigate the effects of a mental health specialist working in tandem with a somatic expert physician in CRDs. If this innovative approach proves successful, it will be made available on a larger scale nationally and promoted internationally. In the best case, ZSE-DUO can significantly shorten the time to diagnosis for a suspected rare disease

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd