Unique immunohistochemical profiles of MUC5AC, MUC6, P53, and Ki67 in gastric atypical hyperplasia and dysplasia.

Objectives: Differentiating gastric atypical hyperplasia (AH) from dysplasia, including low-grade dysplasia (LGD) and high-grade dysplasia (HGD), poses significant challenges in small biopsies and specimens with technical artifacts. This study aims to establish objective diagnostic criteria for these conditions through combined morphologic and immunohistochemical (IHC) analyses.

Methods: Between January 2018 and September 2020, a total of 123 gastric mucosa biopsy specimens were collected at Anyang Tumor Hospital.

A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR.

Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues.

Assessment of NO population exposure from 2005 to 2020 in China.

Nitrogen dioxide (NO) is a major air pollutant with serious environmental and human health impacts. A random forest model was developed to estimate ground-level NO concentrations in China at a monthly time scale based on ground-level observed NO concentrations, tropospheric NO column concentration data from the Ozone Monitoring Instrument (OMI), and meteorological covariates (the MAE, RMSE, and R of the model were 4.16 µg/m, 5.

A long short-term memory-fully connected (LSTM-FC) neural network for predicting the incidence of bronchopneumonia in children.

Bronchopneumonia is the most common infectious disease in children, and it seriously endangers children's health. In this paper, a deep neural network combining long short-term memory (LSTM) layers and fully connected layers was proposed to predict the prevalence of bronchopneumonia in children in Chengdu based on environmental factors and previous prevalence rates. The mean square error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (R) were used to detect the performance of the deep learning model.

Mg storage properties of hollow copper selenide nanocubes.

Rechargeable Mg batteries are thought to be suitable for scalable energy-storage applications because of their high safety and low cost. However, the bivalent Mg2+ cations suffer from sluggish solid-state diffusion kinetics. Herein, a hollow morphological approach is introduced to design copper selenide cathodes for rechargeable Mg batteries.

CoSe hollow microspheres, nano-polyhedra and nanorods as pseudocapacitive Mg-storage materials with fast solid-state Mg diffusion kinetics.

CoSe materials with different nanostructures are used as pseudocapacitive Mg-storage cathodes, which exhibit fast solid-state Mg ions diffusion kinetics. In this work, CoSe with different nanostructures including hollow microspheres (H-CoSe), nano-polyhedra (P-CoSe) and nanorods (R-CoSe) are fabricated by using facile one-step hydrothermal methods, and used as pseudocapacitive electrodes for rechargeable Mg batteries. It is observed that R-CoSe exhibits the highest reversible capacity of 233 mA h g at 50 mA g and an excellent rate capability of 116 mA h g at 500 mA g, ascribing to the 1D nanorod structure which facilitates the solid-state Mg diffusion.

Facile synthesis and electrochemical Mg-storage performance of SbSe nanowires and BiSe nanosheets.

Rechargeable Mg batteries are considered as low-cost and reliable candidates for efficient energy storage, but their development is blocked by the lack of suitable cathode materials. In this work, Sb2Se3 nanowires and Bi2Se3 nanosheets are fabricated by facile one-step hydrothermal methods and their Mg-storage performances are systematically investigated. The results show that the Bi2Se3 nanosheets with stable hierarchical 2D structure exhibit a better performance.

Rechargeable Mg batteries based on a AgS conversion cathode with fast solid-state Mg diffusion kinetics.

Rechargeable Mg batteries are promising candidates for highly safe, large-scale energy storage batteries due to the low-cost and non-dendritic metallic Mg anode. However, exploring high-performance cathodes remains a great challenge blocking their development. Herein, a rechargeable Mg battery is established with a AgS conversion cathode, providing a highly reversible capacity of 120 mA h g at 50 mA g, a superior rate capability of 70 mA h g at 500 mA g, and an outstanding long-term cyclability over 400 cycles.

Rechargeable Mg-M (M = Li, Na and K) dual-metal-ion batteries based on a Berlin green cathode and a metallic Mg anode.

Mg-M (M = Li, Na and K) dual-metal-ion batteries featuring a dendrite-free Mg anode and an alkali-metal-ion storage cathode are promising safe energy storage systems. However, the compatibility between cathode materials and insertion cations might largely limit the electrochemical performance of the cathodes. In this work, three types of Mg-M (M = Li, Na and K) dual-metal-ion batteries are constructed with a Berlin green (FeFe(CN)6) cathode.

a-MoS@CNT nanowire cathode for rechargeable Mg batteries: a pseudocapacitive approach for efficient Mg-storage.

Rechargeable Mg batteries are promising candidates for highly safe large-scale energy storage batteries owing to their low-cost and non-dendritic metallic Mg anode. However, exploration of high-performance cathodes remains a great challenge hindering their development. Herein, a new pseudocapacitive Mg-storage nanowire material (a-MoS3@CNT) is constructed with a carbon nanotube (CNT) core and an amorphous MoS3 (a-MoS3) outer layer (15 nm thick).