A probable case of tick-borne encephalitis (TBE) acquired in England, July 2019

The United Kingdom (UK) has thus far been considered to be free from tick-borne encephalitis (TBE), yet in July 2019, a German infant developed serologically diagnosed TBE following a tick bite in southern England. This first report of a probable human case together with recent findings of TBE virus in ticks in foci in England suggest that TBE may be acquired in parts of England and should be considered in patients with aetiologically-unexplained neurological manifestations.Peer Reviewe

Extending the allelic spectrum at noncoding risk loci of orofacial clefting

Genome-wide association studies (GWAS) have generated unprecedented insights into the genetic etiology of orofacial clefting (OFC). The moderate effect sizes of associated noncoding risk variants and limited access to disease-relevant tissue represent considerable challenges for biological interpretation of genetic findings. As rare variants with stronger effect sizes are likely to also contribute to OFC, an alternative approach to delineate pathogenic mechanisms is to identify private mutations and/or an increased burden of rare variants in associated regions. This report describes a framework for targeted resequencing at selected noncoding risk loci contributing to nonsyndromic cleft lip with/without cleft palate (nsCL/P), the most frequent OFC subtype. Based on GWAS data, we selected three risk loci and identified candidate regulatory regions (CRRs) through the integration of credible SNP information, epigenetic data from relevant cells/tissues, and conservation scores. The CRRs (total 57 kb) were resequenced in a multiethnic study population (1061 patients; 1591 controls), using single-molecule molecular inversion probe technology. Combining evidence from in silico variant annotation, pedigree- and burden analyses, we identified 16 likely deleterious rare variants that represent new candidates for functional studies in nsCL/P. Our framework is scalable and represents a promising approach to the investigation of additional congenital malformations with multifactorial etiology

Dermoscopic evaluation of nodular melanoma

Importance: Nodular melanoma (NM) is a rapidly progressing potentially lethal skin tumor for which early diagnosis is critical

Osteoporosis and bisphosphonates-related osteonecrosis of the jaw: Not just a sporadic coincidence - a multi-centre study

Bisphosphonates (BPs) are powerful drugs that inhibit bone metabolism. Adverse side effects are rare but potentially severe such as bisphosphonate-related osteonecrosis of the jaw (BRONJ). To date, research has primarily focused on the development and progression of BRONJ in cancer patients with bone metastasis, who have received high dosages of BPs intravenously. However, a potential dilemma may arise from a far larger cohort, namely the millions of osteoporosis patients on long-term oral BP therapy

Dermatoskopie – 30 Jahre nach der 1. Konsensus-Konferenz

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A Beta-Sheet Interaction Interface Directs Tetramerisation of the Miz-1 POZ Domain.

The POZ/BTB domain is an evolutionarily conserved motif found in approximately 40 zinc-finger transcription factors (POZ-ZF factors). Several POZ-ZF factors are implicated in human cancer, and POZ domain interaction interfaces represent an attractive target for therapeutic intervention. Miz-1 (Myc-interacting zinc-finger protein) is a POZ-ZF factor that regulates DNA-damage-induced cell cycle arrest and plays an important role in human cancer by virtue of its interaction with the c-Myc and BCL6 oncogene products. The Miz-1 POZ domain mediates both self-association and the recruitment of non-POZ partners. POZ-ZF factors generally function as homodimers, although higher-order associations and heteromeric interactions are known to be physiologically important; crucially, the interaction interfaces in such large complexes have not been characterised. We report here the crystal structure of the Miz-1 POZ domain up to 2.1 Å resolution. The tetrameric organisation of Miz-1 POZ reveals two types of interaction interface between subunits; an interface of alpha-helices resembles the dimerisation interface of reported POZ domain structures, whereas a novel beta-sheet interface directs the association of two POZ domain dimers. We show that the beta-sheet interface directs the tetramerisation of the Miz-1 POZ domain in solution and therefore represents a newly described candidate interface for the higher-order homo- and hetero-oligomerisation of POZ-ZF proteins in vivo

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

Importance: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective: To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants: A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures: The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results: Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P\u2009<\u2009.001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P\u2009=\u2009.001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P\u2009=\u2009.18). Conclusions and Relevance: Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting

Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin

BACKGROUND: Nonpigmented skin cancer is common, and diagnosis with the unaided eye is error prone. OBJECTIVE: To investigate whether dermatoscopy improves the diagnostic accuracy for nonpigmented (amelanotic) cutaneous neoplasms. METHODS: We collected a sample of 2072 benign and malignant neoplastic lesions and inflammatory conditions and presented close-up images taken with and without dermatoscopy to 95 examiners with different levels of experience. RESULTS: The area under the curve was significantly higher with than without dermatoscopy (0.68 vs 0.64, P < .001). Among 51 possible diagnoses, the correct diagnosis was selected in 33.1% of cases with and 26.4% of cases without dermatoscopy (P < .001). For experts, the frequencies of correct specific diagnoses of a malignant lesion improved from 40.2% without to 51.3% with dermatoscopy. For all malignant neoplasms combined, the frequencies of appropriate management strategies increased from 78.1% without to 82.5% with dermatoscopy. LIMITATIONS: The study deviated from a real-life clinical setting and was potentially affected by verification and selection bias. CONCLUSIONS: Dermatoscopy improves the diagnosis and management of nonpigmented skin cancer and should be used as an adjunct to examination with the unaided eye

Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Background: Deep learning convolutional neural networks (CNN) May facilitate melanoma detection, but data comparing a CNN\u2019s diagnostic performance to larger groups of dermatologists are lacking. Methods: Google\u2019s Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists\u2019 diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN\u2019s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (6standard deviation) sensitivity and specificity for lesion classification of 86.6% (69.3%) and 71.3% (611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (69.6%, P \ubc 0.19) and specificity to 75.7% (611.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN\u2019s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians\u2019 experience, they May benefit from assistance by a CNN\u2019s image classification