Matrix effects on the magnetic properties of a molecular spin triangle embedded in a polymeric film

Molecular triangles with competing Heisenberg interactions and significant Dzyaloshinskii–Moriya interactions (DMI) exhibit high environmental sensitivity, making them potential candidates for active elements for quantum sensing. Additionally, these triangles exhibit magnetoelectric coupling, allowing their properties to be controlled using electric fields. However, the manipulation and deposition of such complexes pose significant challenges. This work explores a solution by embedding iron-based molecular triangles in a polymer matrix, a strategy that offers various deposition methods. We investigate how the host matrix alters the magnetic properties of the molecular triangle, with specific focus on the magnetic anisotropy, aiming to advance its practical applications as quantum sensors

Quality of life and clinical characteristics of self-improving congenital ichthyosis within the disease spectrum of autosomal recessive congenital ichthyosis

Background Autosomal-recessive congenital ichthyosis (ARCI) is a heterogeneous group of ichthyoses presenting at birth. Self-improving congenital ichthyosis (SICI) is a subtype of ARCI and is diagnosed when skin condition improves remarkably (within years) after birth. So far, there are sparse data on SICI and quality of life (QoL) in this ARCI subtype. This study aims to further delineate the clinical spectrum of SICI as a rather unique subtype of ARCI. Objectives This prospective study included 78 patients (median age: 15 years) with ARCI who were subdivided in SICI (n = 18) and non-SICI patients (nSICI, n = 60) by their ARCI phenotype. Methods Quality of life (QoL) was assessed using the (Children's) Dermatology Life Quality Index. Statistical analysis was performed with chi-squared and t-Tests. Results The genetically confirmed SICI patients presented causative mutations in the following genes: ALOXE3 (8/16; 50.0%), ALOX12B (6/16; 37.5%), PNPLA1 (1/16; 6.3%) and CYP4F22 (1/16; 6.3%). Hypo-/anhidrosis and insufficient vitamin D levels (<30 ng/mL) were often seen in SICI patients. Brachydactyly (a shortening of the 4th and 5th fingers) was statistically more frequent in SICI (P = 0.023) than in nSICI patients. A kink of the ear's helix was seen in half of the SICI patients and tends to occur more frequently in patients with ALOX12B mutations (P = 0.005). QoL was less impaired in patients under the age of 16, regardless of ARCI type. Conclusions SICI is an underestimated, milder clinical variant of ARCI including distinct features such as brachydactyly and kinking of the ears. Clinical experts should be aware of these features when seeing neonates with a collodion membrane. SICI patients should be regularly checked for clinical parameters such as hypo-/anhidrosis or vitamin D levels and monitored for changes in quality of life

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