Summary
By using oxalic acid (OA) as template and reducer, a novel approach is developed to prepare reduced graphene oxide films with capsular pores (C‐rGOFs) under a hydrothermal condition. The effect of preparation conditions including concentrations of OA and reaction temperatures on the films' structure and capacitive performances has been systematically investigated. The optimal C‐rGOF shows uniform capsule‐like morphology and exhibits a density of 1.18 g cm−3. Tested by using a two‐electrode system, the optimal film shows gravimetric specific capacitance of about 234.9 F g−1 and volumetric specific capacitance of 277.2 F cm−3. Additionally, the optimal film which shows good rate capability can retain 63.9% of initial capacitance at high scan rate of 1.0 V s−1, which is much higher than that of the controlling reduced graphene oxide film (rGOF, 180.5 F g−1, 373.6 F cm−3 and retain only 45.0% of its initial capacitance at 1.0 V s−1). The cells assembled by the optimal C‐rGOF exhibit maximum energy density of 7.5 Wh kg−1, power density of 16.9 kW kg−1, and excellent cycling stability with 91.2% capacitance retention after 21 000 cycles. It is believed that this method can be developed as a useful strategy to prepare rGO‐based materials for energy storage applications.
Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French–American–British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists’ diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.
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