To meet the application needs of rechargeable Zn−air battery and electrocatalytic overall water splitting (EOWS), developing high-efficiency, cost-effective, and durable trifunctional catalysts for the hydrogen evolution reaction (HER), oxygen evolution, and reduction reaction (OER and ORR) is extremely paramount yet challenging. Herein, the interface engineering concept and nanoscale hollowing design were proposed to fabricate N-doping carbon nanoboxes confined with Co/MoC nanoparticles. Uniform zeolitic imidazolate framework nanocube was employed as the starting material to construct the trifunctional electrocatalyst through the conformal polydopamine−Mo layer coating and the subsequent pyrolysis treatment. The Co@IC/MoC@PC catalyst displayed superior electrochemical ORR performances with a positive half-wave potential of 0.875 V and a high limiting current density of 5.89 mA/cm 2 . When practically employed as an electrocatalyst in regenerative Zn−air battery, a high specific capacity of 728 mAh/g, a large peak power density of 221 mW/cm 2 , a high open-circuit voltage of 1.482 V, and a low charge/discharge voltage gap of 0.41 V were obtained. Moreover, its practicability was further exploited by overall water splitting, affording low overpotentials of 277 and 68 mV at 10 mA/cm 2 for the OER and HER in 1 M KOH solution, respectively, and a decent operating potential of 1.57 V for EOWS. Ultraviolet photoelectron spectroscopy and density functional theory calculation revealed that the Co/MoC interface synergistically facilitated the charge-transfer, thereby contributing to the enhancements of electrocatalytic ORR/OER/HER processes. More importantly, this catalyst design concept can offer some interesting prospects for the construction of outstanding trifunctional catalysts toward various energy conversion and storage devices.

Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We collected ultrasound and clinical features from 1,000 breast cancer patients and performed immunohistochemistry on these samples. We used the ensemble decision approach to select unique features and to construct decision models. The decision model for Luminal-A subtype was constructed based on the presence of an echogenic halo and post-acoustic shadowing or indifference. The decision model for Luminal-B subtype was constructed based on the absence of an echogenic halo and vascularity. The decision model for HER2-amplified subtype was constructed based on the presence of post-acoustic enhancement, calcification, vascularity and advanced age. The model for Triple-negative subtype followed two rules. One was based on irregular shape, lobulate margin contour, the absence of calcification and hypovascularity, whereas the other was based on oval shape, hypovascularity and micro-lobulate margin contour. The accuracies of the models were 83.8%, 77.4%, 87.9% and 92.7%, respectively. We identified specific features of each molecular subtype and expanded the scope of ultrasound for making diagnoses using these decision models.

Axillary lymph node metastasis was prone to happen in patients with US features of an irregular tumor shape and higher color Doppler flow imaging grades. Ultrasound imaging provides a promising tool for predicting axillary lymph node metastasis in patients with breast cancer.

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Background Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis. Methods In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set ( n = 542) and independent test set ( n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups. Findings In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6–100) and NSLNs (sensitivity=98.4%, 95% CI 95.6–99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2–100) and 91.7% (95% CI 88.8–97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment. Interpretation The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer. Funding The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.