A time- and dose-dependent STAT1 expression system

BACKGROUND: The signal transducer and activator of transcription (STAT) family of transcription factors mediates a variety of cytokine dependent gene regulations. STAT1 has been mainly characterized by its role in interferon (IFN) type I and II signaling and STAT1 deficiency leads to high susceptibility to several pathogens. For fine-tuned analysis of STAT1 function we established a dimerizer-inducible system for STAT1 expression in vitro and in vivo. RESULTS: The functionality of the dimerizer-induced STAT1 system is demonstrated in vitro in mouse embryonic fibroblasts and embryonic stem cells. We show that this two-vector based system is highly inducible and does not show any STAT1 expression in the absence of the inducer. Reconstitution of STAT1 deficient cells with inducible STAT1 restores IFNγ-mediated gene induction, antiviral responses and STAT1 activation remains dependent on cytokine stimulation. STAT1 expression is induced rapidly upon addition of dimerizer and expression levels can be regulated in a dose-dependent manner. Furthermore we show that in transgenic mice STAT1 can be induced upon stimulation with the dimerizer, although only at low levels. CONCLUSION: These results prove that the dimerizer-induced system is a powerful tool for STAT1 analysis in vitro and provide evidence that the system is suitable for the use in transgenic mice. To our knowledge this is the first report for inducible STAT1 expression in a time- and dose-dependent manner

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

Diet as a moderator in the association of sedentary behaviors with inflammatory biomarkers among adolescents in the HELENA study

AIM: To assess if a healthy diet might attenuate the positive sedentary-inflammation relation, whereas an unhealthy diet may increase the effect of sedentary behaviors on inflammatory biomarkers. METHODS: In 618 adolescents (13-17 years) of the European HELENA study, data were available on body composition, a set of inflammation markers, and food intake assessed by a self-administered computerized 24 h dietary recall for 2 days. A 9-point Mediterranean diet score and an antioxidant-rich diet z-score were used as dietary indices and tested as moderators. A set of low-grade inflammatory characteristics was used as outcome: several cytokines in an inflammatory ratio (IL-6, IL-10, TNF-α, TGFβ-1), C-reactive protein, three cell-adhesion molecules (sVCAM-1, sICAM-1, sE-selectin), three cardiovascular risk markers (GGT, ALT, homocysteine) and three immune cell types (white blood cells, lymphocytes, CD3). Sedentary behaviors were self-reported and analyzed as total screen time. Multiple linear regression analyses tested moderation by diet in the sedentary behaviors-inflammation association adjusted for age, sex, country, adiposity (sum of six skinfolds), parental education, and socio-economic status. RESULTS: Both diet scores, Mediterranean and antioxidant-rich diet, were significant protective moderators in the effect of sedentary behaviors on alanine-transaminase enzyme (P = 0.014; P = 0.027), and on the pro/anti-inflammatory cytokine ratio (P = 0.001; P = 0.004), but not on other inflammatory parameters. CONCLUSION: A higher adherence to the Mediterranean diet or an antioxidant-rich diet may attenuate the onset of oxidative stress signs associated by sedentary behaviors, whereas a poor diet seems to increase inflammation

Dietary patterns and their relationship with the perceptions of healthy eating in European adolescents : the HELENA study

Objective: The aim of this study was to identify dietary patterns (DPs) in European adolescents and to examine the association between perceptions of healthy eating and the obtained DPs. Method: A multinational cross-sectional study was carried out in adolescents aged 12.5 to 17.5?years and 2,027 (44.9% males) were considered for analysis. A self-reported questionnaire with information on food choices and preferences, including perceptions of healthy eating, and two 24-hour dietary recalls were used. Principal component analysis was used to obtain sex-specific DPs, and linear analyses of covariance were used to compare DPs according to perceptions of healthy eating. Results: Three and four DPs for boys and girls were obtained. In boys and girls, there were significant associations between some perceptions about healthy food and the Breakfast-DP (p?<?0.05). In boys, Breakfast-DP and Healthy Beverage-DP were associated with the perception of the own diet as healthy (p?<?0.05). Healthy Beverage-DP was associated with those disliking fruits and vegetables (p?<?0.05). Girls considering the own diet as healthy were associated with Mediterranean-DP, Breakfast-DP, and Unhealthy Beverage and Meat-DP (p?<?0.05). The perception of snacking as a necessary part of a healthy diet was associated with Breakfast-DP in both genders (p?<?0.05). Conclusions: In European adolescents, perceptions of healthy eating were mainly associated with a DP characterized by foods consumed at breakfast. Future studies should further explore these findings in order to implement health promotion programs to improve healthy eating habits in adolescents