Malignant melanoma is a severe and aggressive type of skin cancer, with a rapid decrease in survival rate if not diagnosed and treated at an early stage. Histopathological examination of hematoxylin and eosin stained tissue biopsies under a light microscope is currently the gold standard for diagnosis. However, this manual examination is a difficult and timeconsuming task, and diagnosis is often subject to intra-and inter-observer variability. With more pathology departments starting to convert conventional glass slides into digital resources, a Computer Aided Diagnostic (CAD) system that can automate part of the diagnostic process will help address these challenges. It is expected to reduce examination time, increase diagnostic accuracy, and reduce diagnostic variations. An important initial step in developing such a system is an automated epidermis segmentation algorithm, since several important diagnostic factors are within or seen relatively to the epidermis' location. In this paper, we propose a new epidermis segmentation technique built on Convolutional Neural Networks. We trained an U-net based architecture end-to-end, with ∼ 380k overlapping high resolution image patches at 512 × 512 pixels, extracted and augmented from 36 digitized histopathological images from two different clinical sites, to discriminate pixels as either epidermal or non-epidermal. The proposed technique was evaluated on 33 test images, where we achieved a mean Positive Predictive Value at 0.89 ± 0.16 , Sensitivity at 0.92 ± 0.1 , Dice Similarity Coefficient at 0.89 ± 0.13 and a Matthews Correlation Coefficient at 0.89 ± 0.11 , showing a superior performance when compared to existing techniques. Our algorithm also proves to be robust to variations in staining, tissue thickness and laboratory pre-processing.

Background The precise mechanisms causing cardiac troponin ( cT n) increase after exercise remain to be determined. The aim of this study was to investigate the impact of heart rate (HR) on exercise‐induced cT n increase by using sports watch data from a large bicycle competition. Methods and Results Participants were recruited from NEEDED (North Sea Race Endurance Exercise Study). All completed a 91‐km recreational mountain bike race (North Sea Race). Clinical status, ECG , blood pressure, and blood samples were obtained 24 hours before and 3 and 24 hours after the race. Participants (n=177) were, on average, 44 years old; 31 (18%) were women. Both cTnI and cTnT increased in all individuals, reaching the highest level (of the 3 time points assessed) at 3 hours after the race ( P <0.001). In multiple regression models, the duration of exercise with an HR >150 beats per minute was a significant predictor of both cTnI and cTnT , at both 3 and 24 hours after exercise. Neither mean HR nor mean HR in percentage of maximum HR was a significant predictor of the cT n response at 3 and 24 hours after exercise. Conclusions The duration of elevated HR is an important predictor of physiological exercise‐induced cT n elevation. Clinical Trial Registration URL : https://www.clinicaltrials.gov/ . Unique identifier: NCT 02166216.

Physical exercise is widely recognized as beneficial to the cardiovascular system. However, intense exercise may also carry fatal risk. Investigation of this phenomenon is one of the primary purposes of the North Sea Race Endurance Exercise Study (NEEDED). This paper describes analysis of electrocardiograms (ECG) and heart rate signals collected from amateur athletes, participants of the race, to facilitate noninvasive estimation of the level of cardiac troponin I (cardiovascular risk biomarker) and detection of coronary artery disease (CAD). It was demonstrated that the combination of ECG and heart rate parameters can predict CAD with high specificity (up to 98%) and relatively good sensitivity. Moreover, while troponin level assessment is unlikely to be reliably performed using regression techniques, it might be possible using a new, probabilistic classification-based model. Further evaluation of the latter requires the use of additional data, which is one of possible directions for the future work.