Produktion und funktionelle Charakterisierung humaner rekombinanter Tenascin-R Proteinfragmente in Bezug auf die axonale Regeneration im Zentralnervensystem

Tenascin-R ist ein multimeres Glykoprotein, das ausschließlich in der Extrazellulärmatrix des zentralen Nervensystems vorkommt. Es ist aus strukturell und funktionell unterscheidbaren Domänen aufgebaut und nimmt unterschiedliche Funktionen wahr, welche primär von spezifischen Erkennungsprozessen abhängen, durch die das Molekül mit allen neuralen Zelltypen (Neuronen, Oligodendrozyten, Astrozyten, Mikroglia) in Kontakt tritt. Zur Identifizierung der an diesen Zellerkennungsmechanismen beteiligten Domänen bzw. der Bindungsstellen für zelluläre Rezeptoren und deren Einfluss auf das neurale Zellverhalten wurden humane rekombinante Tenascin-R Fragmente eukaryotisch exprimiert und funktionell charakterisiert. Diese hTNR1-hTNR6 benannten Tenascin-R Fragmente decken die Sequenz des Gesamtmoleküls ab und wurden im FlpIn-293-Expressionssystem von Invitrogen exprimiert. Hierdurch gelang die Herstellung His-getagter sekretierter Proteinfragmente, die anschließend aus Zellkulturüberständen affinitätschromatographisch aufgereinigt werden konnten. Die anschließende biochemische und funktionelle Charakterisierung der so erhaltenen Tenascin-R Fragmente geschah primär im Kontext zellulären Verhaltens (neurale Zelladhäsion sowie axonales Wachstum) unter Berücksichtigung der daran beteiligten molekularen Rezeptorsysteme. Im Rahmen der hierbei durchgeführten Studien ist es gelungen, Moleküldomänen zu identifizieren, die für die Wirkung von Tenascin-R als selektives Substrat für Oligodendrozyten verantwortlich sind. Diesen bisher nur vom Gesamtmolekül bekannten Erkennungsprozess machte sich schon ein durch Heraclitus Biosciences patentiertes Verfahren zu nutze, mit welchem aus einem Gemisch aller Gehirnzellen Oligodendrozyten durch selektive Zelladhäsion in einem Schritt isoliert werden können

A novel multiparametric imaging approach to acute myocarditis using T2-mapping and CMR feature tracking

Background: The aim of this study was to evaluate the diagnostic potential of a novel cardiovascular magnetic resonance (CMR) based multiparametric imaging approach in suspected myocarditis and to compare it to traditional Lake Louise criteria (LLC). Methods: CMR data from 67 patients with suspected acute myocarditis were retrospectively analyzed. Seventeen age- and gender-matched healthy subjects served as control. T2-mapping data were acquired using a Gradient-Spin-Echo T2-mapping sequence in short-axis orientation. T2-maps were segmented according to the 16-segments AHA-model and segmental T2 values and pixel-standard deviation (SD) were recorded. Afterwards, the parameters maxT2 (the highest segmental T2 value) and madSD (the mean absolute deviation (MAD) of the pixel-SDs) were calculated for each subject. Cine sequences in three long axes and a stack of short-axis views covering the left and right ventricle were analyzed using a dedicated feature tracking algorithm. Results: A multiparametric imaging model containing madSD and LV global circumferential strain (GCSLV) resulted in the highest diagnostic performance in receiver operating curve analyses (area under the curve [AUC] 0.84) when compared to any model containing a single imaging parameter or to LLC (AUC 0.79). Adding late gadolinium enhancement (LGE) to the model resulted in a further increased diagnostic performance (AUC 0.93) and yielded the highest diagnostic sensitivity of 97% and specificity of 77%. Conclusions: A multiparametric CMR imaging model including the novel T2-mapping derived parameter madSD, the feature tracking derived strain parameter GCSLV and LGE yields superior diagnostic sensitivity in suspected acute myocarditis when compared to any imaging parameter alone and to LLC. © 2017 The Author(s)

An overview and a roadmap for artificial intelligence in hematology and oncology.

Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals

Biventricular myocardial strain analysis in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) using cardiovascular magnetic resonance feature tracking

BACKGROUND: Fibrofatty degeneration of myocardium in ARVC is associated with wall motion abnormalities. The aim of this study was to examine whether Cardiovascular Magnetic Resonance (CMR) based strain analysis using feature tracking (FT) can serve as a quantifiable measure to confirm global and regional ventricular dysfunction in ARVC patients and support the early detection of ARVC. METHODS: We enrolled 20 patients with ARVC, 30 with borderline ARVC and 22 subjects with a positive family history but no clinical signs of a manifest ARVC. 10 healthy volunteers (HV) served as controls. 15 ARVC patients received genotyping for Plakophilin-2 mutation (PKP-2), of which 7 were found to be positive. Cine MR datasets of all subjects were assessed for myocardial strain using FT (TomTec Diogenes Software). Global strain and strain rate in radial, circumferential and longitudinal mode were assessed for the right and left ventricle. In addition strain analysis at a segmental level was performed for the right ventricular free wall. RESULTS: RV global longitudinal strain rates in ARVC (−0.68 ± 0.36 sec(−1)) and borderline ARVC (−0.85 ± 0.36 sec(−1)) were significantly reduced in comparison with HV (−1.38 ± 0.52 sec(−1), p ≤ 0.05). Furthermore, in ARVC patients RV global circumferential strain and strain rates at the basal level were significantly reduced compared with HV (strain: −5.1 ± 2.7 vs. -9.2 ± 3.6%; strain rate: −0.31 ± 0.13 sec(−1) vs. -0.61 ± 0.21 sec(−1)). Even for patients with ARVC or borderline ARVC and normal RV ejection fraction (n=30) global longitudinal strain rate proved to be significantly reduced compared with HV (−0.9 ± 0.3 vs. -1.4 ± 0.5 sec(−1); p < 0.005). In ARVC patients with PKP-2 mutation there was a clear trend towards a more pronounced impairment in RV global longitudinal strain rate. On ROC analysis RV global longitudinal strain rate and circumferential strain rate at the basal level proved to be the best discriminators between ARVC patients and HV (AUC: 0.9 and 0.92, respectively). CONCLUSION: CMR based strain analysis using FT is an objective and useful measure for quantification of wall motion abnormalities in ARVC. It allows differentiation between manifest or borderline ARVC and HV, even if ejection fraction is still normal

AutoRadiomics: a framework for reproducible radiomics research

Purpose Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities. Methods The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets—Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx. Results In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77. Conclusion AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results

PURPOSE To test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches. METHODS This retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images. RESULTS Comparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity. CONCLUSIONS TA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations

Machine learning in cardiovascular magnetic resonance : basic concepts and applications

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups

Machine learning in cardiovascular magnetic resonance : basic concepts and applications

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups