An unusual timing for symptomatic chest pain in an adult chest wall myofibroma: a case report

INTRODUCTION: Myofibromas are benign mesenchymal neoplasms that can present as solitary and multicentric lesions. They can occur in several locations and can occur at any age from neonates to elderly patients. However, most of the lesions are found in neonates and babies. It rarely occurs in adults. CASE PRESENTATION: A 29-year-old Taiwanese man presented with persistent dull chest pain in his right lateral chest wall for 2 weeks. A chest X-ray showed a faint patchy opacity over the periphery of his right upper lung zone. Computed tomography and magnetic resonance imaging showed a lobulated mass at the intercostal space between his right fifth and sixth ribs with contrast enhancement and bone invasion. Malignancy could not be excluded. A percutaneous needle aspiration biopsy failed due to technique issues, so he underwent a thoracotomy and the tumor was excised with Marlex mesh repairs for the thoracic defect. Pathology confirmed a myofibroma without malignancy. He recovered uneventfully and no local recurrence was detected at the 1-year follow-up examination. CONCLUSIONS: Chest wall myofibroma presenting with chest pain has never been reported in adults. It is a challenge to differentiate myofibroma from malignancy in chest wall preoperatively, such as seen in our patient. To the best of our knowledge, this has not been previously reported in the scientific literature. Although myofibroma rarely occurs in the chest wall and adults, it must be suspected in any chest wall tumor presenting with chest pain

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.Comment: Accepted at ICIP 201

Kondo Signatures of a Quantum Magnetic Impurity in Topological Superconductors

We study the Kondo physics of a quantum magnetic impurity in two-dimensional topological superconductors (TSCs), either intrinsic or induced on the surface of a bulk topological insulator, using a numerical renormalization group technique. We show that, despite sharing the p+ip pairing symmetry, intrinsic and extrinsic TSCs host different physical processes that produce distinct Kondo signatures. Extrinsic TSCs harbor an unusual screening mechanism involving both electron and orbital degrees of freedom that produces rich and prominent Kondo phenomena, especially an intriguing pseudospin Kondo singlet state in the superconducting gap and a spatially anisotropic spin correlation. In sharp contrast, intrinsic TSCs support a robust impurity spin doublet ground state and an isotropic spin correlation. These findings advance fundamental knowledge of novel Kondo phenomena in TSCs and suggest experimental avenues for their detection and distinction

Seismology-based early identification of dam-formation landquake events

Flooding resulting from the bursting of dams formed by landquake events such as rock avalanches, landslides and debris flows can lead to serious bank erosion and inundation of populated areas near rivers. Seismic waves can be generated by landquake events which can be described as time-dependent forces (unloading/reloading cycles) acting on the Earth. In this study, we conduct inversions of long-period (LP, period ≥20 s) waveforms for the landquake force histories (LFHs) of ten events, which provide quantitative characterization of the initiation, propagation and termination stages of the slope failures. When the results obtained from LP waveforms are analyzed together with high-frequency (HF, 1–3 Hz) seismic signals, we find a relatively strong late-arriving seismic phase (dubbed Dam-forming phase or D-phase) recorded clearly in the HF waveforms at the closest stations, which potentially marks the time when the collapsed masses sliding into river and perhaps even impacting the topographic barrier on the opposite bank. Consequently, our approach to analyzing the LP and HF waveforms developed in this study has a high potential for identifying five dam-forming landquake events (DFLEs) in near real-time using broadband seismic records, which can provide timely warnings of the impending floods to downstream residents