Magnetic wallpaper Dirac fermions and topological magnetic Dirac insulators

Topological crystalline insulators (TCIs) can host anomalous surface states which inherits the characteristics of crystalline symmetry that protects the bulk topology. Especially, the diversity of magnetic crystalline symmetries indicates the potential for novel magnetic TCIs with distinct surface characteristics. Here, we propose a topological magnetic Dirac insulator (TMDI), whose two-dimensional surface hosts fourfold-degenerate Dirac fermions protected by either the $p'_c4mm$ or $p4'g'm$ magnetic wallpaper group. The bulk topology of TMDIs is protected by diagonal mirror symmetries, which give chiral dispersion of surface Dirac fermions and mirror-protected hinge modes. We propose candidate materials for TMDIs including Nd$_4$Te$_8$Cl$_4$O$_{20}$ and DyB$_4$ based on first-principles calculations, and construct a general scheme for searching TMDIs using the space group of paramagnetic parent states. Our theoretical discovery of TMDIs will facilitate future research on magnetic TCIs and illustrate a distinct way to achieve anomalous surface states in magnetic crystals.Comment: 10+36 pages, 4+23 figures, published versio

Idiopathic Spontaneous Rupture of a Subcostal Artery in a Patient Undergoing Hemodialysis: A Case Report

The spontaneous rupture of a subcostal (12th intercostal) artery is exceptionally rare and could be fatal, requiring early diagnosis and treatment. Only one case of intercostal artery (ICA) bleeding in a patient undergoing hemodialysis (HD) has been reported. We additionally describe a 41-year-old man undergoing HD who presented with a spontaneous hemoperitoneum and shock resulting from a subcostal artery rupture. He initially complained of diffuse abdominal pain and dizziness at the emergency room. His abdomen was bloated, and there was tenderness in the right upper quadrant area. Enhanced computed tomography and arteriography revealed a rupture of the right subcostal artery. After the super-selection of the bleeding artery by a microcatheter, embolization was performed using a detachable coil and gelfoam. In a subsequent arteriogram, additional contrast leakage was no longer detected, and his blood pressure was restored to normal. The patient was discharged without any sequelae. He was followed up at our HD center without recurrence of ICA bleeding. To the best of our knowledge, this is the second case in the English literature documenting a spontaneous ICA rupture in a patient undergoing HD. This case indicates that injury to ICA should be suspected when patients undergoing HD complain of abdominal or chest pain and dizziness, although it is very rare

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1–5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1–5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer–Cuzick (TC) model and Gail model, using DeLong’s test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1–5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction