Assessing the risk of cardiovascular events in patients receiving immune checkpoint inhibitors.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, despite their excellent therapeutic effect, these medications typically result in a broad spectrum of toxicity reactions. Immune-related cardiotoxicity is uncommon but can be potentially fatal, and its true incidence is underestimated in clinical trials. The aim of this study is to assess the incidence and identify risk factors for developing a cardiac event in patients treated with ICIs. Methods: We conducted a single-institution retrospective study, including patients treated with ICIs in our center. The main outcomes were cardiac events (CE) and cardiovascular death. Results: A total of 378 patients were analyzed. The incidence of CE was 16.7%, during a median follow-up of 50.5 months. The multivariable analysis showed that age, a history of arrhythmia or ischemic heart disease, and prior immune-related adverse events were significantly associated with CE. Conclusion: CE during ICI treatment are more common than currently appreciated. A complete initial cardiovascular evaluation is recommended, especially in high-risk patients, being necessary a multidisciplinary approach of a specialized cardio-oncology team.

From phenotype to genotype in complex brain networks.

Generative models are a popular instrument for illuminating the relationships between the hidden variables driving the growth of a complex network and its final topological characteristics, a process known as the "genotype to phenotype problem". However, the definition of a complete methodology encompassing all stages of the analysis, and in particular the validation of the final model, is still an open problem. We here discuss a framework that allows to quantitatively optimise and validate each step of the model creation process. It is based on the execution of a classification task, and on estimating the additional precision provided by the modelled genotype. This encompasses the three main steps of the model creation, namely the selection of topological features, the optimisation of the parameters of the generative model, and the validation of the obtained results. We provide a minimum requirement for a generative model to be useful, prescribing the function mapping genotype to phenotype to be non-monotonic; and we further show how a previously published model does not fulfil such condition, casting doubts on its fitness for the study of neurological disorders. The generality of such framework guarantees its applicability beyond neuroscience, like the emergence of social or technological networks.

Mining recurring concepts in a dynamic feature space.

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.