Novice students still have problems with the abstraction of object-oriented programming. This research shows that the debugger could be used to clarify abstraction of the object-oriented programming with the C++ program language. Our investigation spans two consecutive courses on object-oriented programming. For the introduction course on the object-oriented programming, we modified both lectures and lab sessions so that with the help of the debugger objects could be visualised, and we could walk through the program step by step. In the second course, the lectures were extended by forty-five minutes, allowing the students to do assignments during the lectures with the help of the debugger. The results of the written examination and the completed questionnaire showed that working with the debugger in both the lectures and the practical exercises had a positive influence, especially for the introductory course on object-oriented programming. Although the Corona pandemic influenced part two, the results of the written exam did improve, but it is unclear which influence the use of the debugger had during this course. Visualising objects and walking through methods step by step could help in understanding the fundamentals of object-oriented programming language C++.

The physical and social distancing measures that have been adopted worldwide because of COVID-19 will probably remain in place for a long time, especially for senior adults, people with chronic conditions, and other at-risk populations. Teleconsultations can be useful in ensuring that patients continue to receive clinical care while reducing physical crowding and avoiding unnecessary exposure of health care staff. Implementation processes that typically take months of planning, budgeting, pilot testing, and education were compressed into days. However, in the urgency to deal with the present crisis, we may be forgetting that the introduction of digital health is not exclusively a technological issue, but part of a complex organizational change problem. This viewpoint offers insight regarding issues that rapidly adopted teleconsultation systems may face in a post–COVID-19 world.@en

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types. Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset. Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74. Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.@en