A fiber-shaped aqueous lithium ion battery is developed with ultrafast charge–discharge rates and high power density in addition to high energy density.

Patients with acute pancreatitis are liable to suffer from PUD. PUD is associated with severe acute pancreatitis according to the APACHE II score, and treatment for PUD should be considered for patients with severe acute pancreatitis.

Laparoscopy is feasible and safe for the diagnosis and treatment of hemodynamically stable patients with blunt abdominal trauma and can reduce the laparotomy rate.

Breast cancer is a heterogeneous disease. Thanks to extensive efforts from research scientists and clinicians, treatment for breast cancer has advanced into the era of targeted medicine. With the use of several well-established biomarkers, such as hormone receptors (HRs) (i.e. estrogen receptor [ER] and progesterone receptor [PgR]) and Human Epidermal Growth Factor Receptor-2 (HER2), breast cancer patients can be categorized into multiple subgroups with specific targeted treatment strategies. Although therapeutic strategies for HR-positive (HR+) HER2-negative (HER2−) breast cancer and HR-negative (HR−) HER2-positive (HER2+) breast cancer are well-defined, HR+ HER2+ breast cancer is still an overlooked subgroup without tailored therapeutic options. In this review, we have summarized the molecular characteristics, etiology, preclinical tools and therapeutic options for HR+ HER2+ breast cancer. We hope to raise the attention of both the research and the medical community on HR+ HER2+ breast cancer, and to advance patient care for this subtype of disease.

Automated whole breast ultrasound (ABUS) has become a popular screening tool in recent years. To reduce the review time and misdetection from ABUS images by physicians, a computer-aided detection (CADe) system for ABUS images based on a multiview method is proposed in this study. A total of 58 pathology-proven lesions from 41 patients were used to evaluate the performance of the system. In the proposed CADe system, the fuzzy c-mean clustering method was applied to detect tumor candidates from these ABUS images. Subsequently, the tumor likelihoods of these candidates could be estimated by a logistic linear regression model based on the intensity, morphology, location, and size features in the transverse, longitudinal, and coronal views. Finally, the multiview tumor likelihoods of the tumor candidates could be obtained from the estimated tumor likelihoods of the three views, and the tumor candidates with high multiview tumor likelihoods were regarded as the detected tumors in the proposed system. The sensitivities of the multiview tumor detection for selecting 5, 10, 20, and 30 tumor candidates with the largest multiview tumor likelihoods were 79.31%, 86.21%, 96.55%, and 98.28%, respectively.