Evaluation of capillary pathologies by nailfold capillaroscopy in patients with psoriasis vulgaris: study protocol for a prospective, controlled exploratory study.

INTRODUCTION: Psoriasis vulgaris was shown to be an independent factor increasing the risk of several comorbidities such as obesity, diabetes and dyslipidaemia with an increased risk of stroke and myocardial infarction. We hypothesise that early endothelial dysfunction, which plays a crucial role in the pathogenesis of atherosclerosis, may be detected by digital video nailfold capillaroscopy (DVNC) at the level of the dermal capillary microvasculature as a surrogate parameter. Nailfolds represent the only body site allowing for a non-invasive assessment of the capillary microvasculature at a horizontal plane. DVNC is a well-established diagnostic tool for in vivo assessment of the peripheral microcirculation by evaluating the morphology of dermal papillary capillaries. To date, reports on morphological changes of the non-lesional nailfold capillaries in patients with psoriasis vulgaris are scarce and the existing data are not conclusive. METHODS AND ANALYSIS: This is a prospective, single-centre, non-randomised, controlled, exploratory study assessing the capillary patterns in 100 subjects affected by psoriasis vulgaris. Non-lesional nailfold capillaries will be imaged by means of DVNC (Optilia Digital Capillaroscopy System, Optilia Instruments AB, Sollentuna, Sweden) in 50 patients affected by psoriasis vulgaris and 50 healthy controls. Assessments will include a qualitative, descriptive analysis of the nailfold capillaries' morphology, as well as a quantitative investigation (frequency, extent) of changes in capillary patterns. Moreover, patients' characteristics associated with the manifestation of nailfold capillaries' pathologies including well-known cardiovascular risk markers will be studied. ETHICS AND DISSEMINATION: Ethical approval was provided by the ethic committee of the medical faculty of the University of Heidelberg (Ethics approval number S-447/2017). The design and the final results of the study will be published and made available to the public. TRIAL REGISTRATION NUMBER: DRKS00012856.

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's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's 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' 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's 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 (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, 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's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Foxb1 Regulates Negatively the Proliferation of Oligodendrocyte Progenitors.

Oligodendrocyte precursor cells (OPC), neurons and astrocytes share a neural progenitor cell (NPC) in the early ventricular zone (VZ) of the embryonic neuroepithelium. Both switch to produce either of the three cell types and the generation of the right number of them undergo complex genetic regulation. The components of these regulatory cascades vary between brain regions giving rise to the unique morphological and functional heterogeneity of this organ. Forkhead b1 (Foxb1) is a transcription factor gene expressed by NPCs in specific regions of the embryonic neuroepithelium. We used the mutant mouse line Foxb1-Cre to analyze the cell types derived from Fobx1-expressing NPCs (the Foxb1 cell lineage) from two restricted regions, the medulla oblongata (MO; hindbrain) and the thalamus (forebrain), of normal and Foxb1-deficient mice. Foxb1 cell lineage derivatives appear as clusters in restricted regions, including the MO (hindbrain) and the thalamus (forebrain). Foxb1-expressing NPCs produce mostly oligodendrocytes (OL), some neurons and few astrocytes. Foxb1-deficient NPCs generate mostly OPC and immature OL to the detriment of neurons, astrocytes and mature OL. The axonal G-ratio however is not changed. We reveal Foxb1 as a novel modulator of neuronal and OL generation in certain restricted CNS regions. Foxb1 biases NPCs towards neuronal generation and inhibits OPC proliferation while promoting their differentiation.