A randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students.

Purpose: Self-testing has been proven to significantly improve not only simple learning outcomes, but also higher-order skills such as clinical reasoning in medical students. Previous studies have shown that self-testing was especially beneficial when it was presented with feedback, which leaves the question whether an immediate and personalized feedback further encourages this effect. Therefore, we hypothesised that individual feedback has a greater effect on learning outcomes, compared to generic feedback.

Digital Technologies in Hereditary Coagulation Disorders: A Systematic Review.

Background:  This systematic review aims to comprehensively survey digital technologies used in the prevention, diagnosis, and treatment of hereditary blood coagulation disorders.

Methods:  The systematic review was performed according to the PRISMA guidelines. A systematic search was conducted on PubMed on January 29, 2024.

Monitoring individualized glucose levels predicts risk for bradycardia in type 2 diabetes patients with chronic kidney disease: a pilot study.

Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) exhibit an elevated risk for cardiac arrhythmias, such as bradycardia, which may potentially lead to sudden cardiac death (SCD). While hypoglycemia, defined as a critical drop in glucose levels below the normal range, has long been associated with adverse cardiovascular events, recent studies have highlighted the need for a comprehensive reevaluation of its direct impact on cardiovascular outcomes, particularly in high-risk populations such as those with DM and CKD. In this study, we investigated the association between glucose levels and bradycardia by simultaneously monitoring interstitial glucose (IG) and ECG for 7 days in insulin-treated patients with DM and CKD.

Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder.

Wearable technology enables the unsupervised recording of electrocardiogram (ECG) signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical approaches and explainability. This work investigates the feasibility of medically explainable anomaly detection through disentangled representational learning of ECGs and personalization to mitigate inter-subject variations.

Cold Atmospheric Plasma Induces Growth Arrest and Apoptosis in Neurofibromatosis Type 1-Associated Peripheral Nerve Sheath Tumor Cells.

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder resulting from mutations in the gene. Patients harboring these mutations are predisposed to a spectrum of peripheral nerve sheath tumors (PNSTs) originating from Schwann cells, of which malignant peripheral nerve sheath tumors (MPNSTs) are the deadliest, with limited treatment options. Therefore, an unmet need still exists for more effective therapies directed at these aggressive malignancies.