Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance.

Importance: Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis.

Design And Patients: In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation).

A genome-wide association study on hematopoietic stem cell transplantation reveals novel genomic loci associated with transplant outcomes.

Introduction: Data on genomic susceptibility for adverse outcomes after hematopoietic stem cell transplantation (HSCT) for recipients are scarce.

Methods: We performed a genome wide association study (GWAS) to identify genes associated with survival/mortality, relapse, and severe graft-versus-host disease (sGvHD), fitting proportional hazard and subdistributional models to data of n=1,392 recipients of European ancestry from three centres.

Results: The single nucleotide polymorphism (SNP) rs17154454, intronic to the neuronal growth guidant semaphorin 3C gene (, was genome-wide significantly associated with event-free survival (p=7.

Confirmation of Dissipative Sensing Enhancement in a Microresonator Using Multimode Input.

Optical microresonators have proven to be especially useful for sensing applications. In most cases, the sensing mechanism is dispersive, where the resonance frequency of a mode shifts in response to a change in the ambient index of refraction. It is also possible to conduct dissipative sensing, in which absorption by an analyte causes measurable changes in the mode linewidth and in the throughput dip depth.

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.

Importance: Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking.

Objective: To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions.

ImputAccur: fast and user-friendly calculation of genotype-imputation accuracy-measures.

Background: ImputAccur is a software tool to measure genotype-imputation accuracy. Imputation of untyped markers is a standard approach in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy for imputed genotypes is fundamental.

Deciphering associations between three RNA splicing-related genetic variants and lung cancer risk.

Limited efforts have been made in assessing the effect of genome-wide profiling of RNA splicing-related variation on lung cancer risk. In the present study, we first identified RNA splicing-related genetic variants linked to lung cancer in a genome-wide profiling analysis and then conducted a two-stage (discovery and replication) association study in populations of European ancestry. Discovery and validation were conducted sequentially with a total of 29,266 cases and 56,450 controls from both the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium as well as the OncoArray database.

Does sex matter? Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection.

Background: Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany).

Gene-gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer.

Background: Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility.

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Hintergrund: Die Psoriasis gilt als unabhängiger kardiovaskulärer Risikofaktor und Treiber einer Atherogenese. Mikrovaskuläre Veränderungen in psoriatischen Plaques sind gut beschrieben, wohingegen Veränderungen außerhalb betroffener Hautareale kaum untersucht wurden. In dieser Studie wurden Nagelfalzkapillaren von Psoriasispatienten in nicht betroffener Haut systematisch untersucht.

Nailfold videocapillaroscopy identifies microvascular pathologies in psoriasis vulgaris: Results of a prospective controlled study.

Background: Psoriasis is considered an independent cardiovascular risk factor, evidentially driving atherosclerosis. However, little is known about changes in the microvasculature of non-lesional skin in psoriasis patients. This study systematically examined capillary pathologies in psoriasis patients by digital video nailfold capillaroscopy.

Context-dependent modulation of aggressiveness of pediatric tumors by individual oncogenic RAS isoforms.

A prototypic pediatric cancer that frequently shows activation of RAS signaling is embryonal rhabdomyosarcoma (ERMS). ERMS also show aberrant Hedgehog (HH)/GLI signaling activity and can be driven by germline mutations in this pathway. We show, that in ERMS cell lines derived from sporadic tumors i.

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Hintergrund Und Ziele: Systeme künstlicher Intelligenz (durch "deep learning" faltende neuronale Netzwerke; engl. convolutional neural networks, CNN) erreichen inzwischen bei der Klassifikation von Hautläsionen vergleichbar gute Ergebnisse wie Dermatologen. Allerdings müssen die Limitationen solcher Systeme vor flächendeckendem klinischem Einsatz bekannt sein.

Dark corner artefact and diagnostic performance of a market-approved neural network for skin cancer classification.

Background And Objectives: Convolutional neural networks (CNN) have proven dermatologist-level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence of a dark tubular periphery in dermatoscopic images (also called dark corner artefact [DCA]) on the diagnostic performance of a market-approved CNN for skin lesion classification was investigated.

Skin lesions of face and scalp - Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.

Background: The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking.

Methods: A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL.

Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.

Background: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation and weighting of handcrafted features, to a CNN trained by deep learning.

Methods: Cross-sectional study using a real-world, prospectively acquired, dermoscopic dataset of 1981 skin lesions to compare the diagnostic performance of a market-approved CNN (Moleanalyzer-Pro™, developed in 2018) to a CIA (Moleanalyzer-3™/Dynamole™; developed in 2004, all FotoFinder Systems Inc, Germany).

Protein-altering germline mutations implicate novel genes related to lung cancer development.

Few germline mutations are known to affect lung cancer risk. We performed analyses of rare variants from 39,146 individuals of European ancestry and investigated gene expression levels in 7,773 samples. We find a large-effect association with an ATM L2307F (rs56009889) mutation in adenocarcinoma for discovery (adjusted Odds Ratio = 8.

Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

Background: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among others depends on melanoma localisation and subtype (e.g.

Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios.

In this paper, we propose the class of generalized additive models for location, scale and shape in a test for the association of genetic markers with non-normally distributed phenotypes comprising a spike at zero. The resulting statistical test is a generalization of the quantitative transmission disequilibrium test with mating type indicator, which was originally designed for normally distributed quantitative traits and parent-offspring data. As a motivational example, we consider coronary artery calcification (CAC), which can accurately be identified by electron beam tomography.

Global, pathway and gene coverage of three Illumina arrays with respect to inflammatory and immune-related pathways.

Genome-wide association studies have led in the past to the discovery of susceptibility genes for many diseases including cancer and inflammatory conditions. However, a number of these studies did not realise their full potential. A first critical step in developing such large-scale studies is the choice of genotyping array with respect to the study goal.

Genetic interaction analysis among oncogenesis-related genes revealed novel genes and networks in lung cancer development.

The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterations and tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study.