Sclerosing angiomatoid nodular transformation (SANT) of the spleen: a case report.

SANT is a rare, non-lymphoid, benign entity, originating from the red pulp of the spleen. It is characterized by the presence of vascular nodules surrounded by a stroma of collagen fibers. It was introduced as a distinct disease entity by Martel et al in 2004, after the histopathological examination of 25 cases.

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We performed an observational two-center study in order to develop a machine learning algorithm to proactively detect arterial hypertension from single-lead ECGs.

Targeted Energy Transfer Dynamics and Chemical Reactions.

Ultrafast reaction processes take place when resonant features of nonlinear model systems are taken into account. In the targeted energy or electron transfer dimer model this is accomplished through the implementation of nonlinear oscillators with opposing types of nonlinearities, one attractive while the second repulsive. In the present work, we show that this resonant behavior survives if we take into account the vibrational degrees of freedom as well.

The circular economy through the prism of machine learning and the YouTube video media platform.

The transition to a Circular Economy (CE) is rapidly gaining ground across countries and industries. It is the means of achieving more sustainable development by adopting innovative environmentally friendly strategies and saving primary resources. There are several studies indicating the increasing public and corporate interest in the CE but still remain limited in terms of the multitude and utilization of social media data.

Revisiting the use of the EORTC QLQ-STO22 to assess health-related quality of life of patients with gastric cancer: incorporating updated treatment options and cross-cultural perspectives.

Background: The EORTC QLQ-STO22 (QLQ-STO22) is a firmly established and validated measure of health-related quality of life (HRQoL) for people with gastric cancer (GC), developed over two decades ago. Since then there have been dramatic changes in treatment options for GC. Also, East Asian patients were not involved in the development of QLQ-STO22, where GC is most prevalent and the QLQ-STO22 is widely used.