CEBPA-Mutationen bei jüngeren Erwachsenen mit akuter myeloischer Leukämie und normalem Karyotyp

CEBPA (CCAAT/enhancer binding protein alpha) is a transcription factor which plays an important role in the differentiation of myeloid progenitor cells into granulocytes. The purpose of this thesis was the analysis of the incidence, the molecular characteristics and the prognostic relevance of CEBPA-mutations in patients with acute myeloid leukaemia (AML). The entire CEBPA-gene was sequenced in diagnostic samples of 122 AML-patients aged 16 to 60 years, homogeneously treated on a protocol of the AML Study Group Ulm. The focus lay on the group of patients with normal karyotype, which is classified as intermediate risk. Within this group of 84 patients, 13 % had a CEBPA-mutation, 7 % had more than one CEBPA-mutation. Different types of mutations could be identified: N-terminal loss-of-function-mutations (in 6 %) affect the transactivation-domains and lead to the synthesis of a shorter dominant-negative CEBPA-isoform. Mutations of the C-terminal basic-region-leucine-zipper domain were found as frameshift- (in 4 %) or in-frame-mutations (in 5 %). They are predicted to impair the binding of CEBPA to the DNA. N-terminal loss-of-function-mutations and C-terminal in-frame mutations often coincided; they were localized on different alleles. There was a striking accumulation of the FAB-subtypes M1 and M2 among the patients with a CEBPA-mutation. The data of this work flew into a retrospective study with 236 AML-patients with normal cytogenetics to assess the prognostic relevance of CEBPA-mutations. Patients with a CEBPA-mutation showed a significantly longer remission duration (p = .01) and overall survival (p = .05). The CEBPA-mutation-status was an independent prognostic factor. In the future, the analysis of the CEBPA-mutation-status may improve the risk stratification in AML by identifying patients with a favourable outcome within the clinically heterogeneous group of patients with normal cytogenetics and take influence on new risk adapted therapy protocols

Dangerous Leg Cramps: Severe Pustular Exanthema Caused by an Over-the-Counter Drug

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Genome-wide Association Study Identifies 2 New Loci Associated With Anti-NMDAR Encephalitis

Background and Objectives To investigate the genetic determinants of the most common type of antibody-mediated autoimmune encephalitis, anti-NMDA receptor (anti-NMDAR) encephalitis. Methods We performed a genome-wide association study in 178 patients with anti-NMDAR encephalitis and 590 healthy controls, followed by a colocalization analysis to identify putatively causal genes. Results We identified 2 independent risk loci harboring genome-wide significant variants (p = 2.2), 1 on chromosome 15, harboring only the LRRK1 gene, and 1 on chromosome 11 centered on the ACP2 and NR1H3 genes in a larger region of high linkage disequilibrium. Colocalization signals with expression quantitative trait loci for different brain regions and immune cell types suggested ACP2, NR1H3, MADD, DDB2, and C11orf49 as putatively causal genes. The best candidate genes in each region are LRRK1, encoding leucine-rich repeat kinase 1, a protein involved in B-cell development, and NR1H3 liver X receptor alpha, a transcription factor whose activation inhibits inflammatory processes. Discussion This study provides evidence for relevant genetic determinants of antibody-mediated autoimmune encephalitides outside the human leukocyte antigen (HLA) region. The results suggest that future studies with larger sample sizes will successfully identify additional genetic determinants and contribute to the elucidation of the pathomechanism

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd