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Background and purpose: It is still a great challenge to clarify the signal molecules that mediate the communication between cancer-associated fibroblasts (CAFs) and tumor cells. These signal molecules are very important for cancer metastasis. The purpose of this study was to explore the communication mechanism of pleckstrin-2/miR-196a signal axis mediated by lung cancer cells in tumor microenvironment. Methods: Human lung adenocarcinoma cell line H1299 and human embryonic lung cell MRC-5 were selected as the research objects. H1299 cells were transfected with lentivirus (PLEK2) expressing PLEK2 and Vector control, and exosomes (Vector_exo, PLEK2_exo) were isolated after 24 h of transfection. MRC-5 cells were transfected with miR-196a mimetic or inhibitor. The expressions of PLEK2 and epithelial-mesenchymal transition (EMT)-related proteins were analyzed by Western blot. The expression of miR-196a was analyzed by polymerase chain reaction (PCR), and the metastasis and invasion ability of cells were determined by transwell assay. Six female BALB/c-nu mice were randomly divided into Vector group and PLEK2 group, with 3 mice in each group. Mice in each group were injected with H1299 cells transfected with Vector or PLEK2 through the tail vein. After 4 weeks, lung tissue was taken out for H-E staining and immunohistochemical staining to analyze the expression of α-smooth muscle actin (α-SMA). All animal experiments were approved by the ethics committee of First Hospital of Changsha City (Changsha Hospital, Xiangya School of Medicine, Central South University) (ethics number: EI-2021-103). Results: Compared with the Vector group, the number of pulmonary metastatic nodules and the expression of α-SMA in metastatic cancer in PLEK2 group increased significantly (P

The low contrast and blurry facial features of facial images collected by the monitoring system are caused by dim illumination and interference from artificial illumination sources in coal mines. Traditional facial detection algorithms may cause false or missed detections when applied in coal mines. In order to solve the above problems, a deep learning-based face detection method under low illumination conditions in coal mines is proposed. A generative adversarial network (GAN) based on unsupervised learning is used to enhance the contrast of low illumination images in coal mines. A self-adjusting attention guided U-Net is used as the generator, and dual discriminators are used to guide global and local information. The self-feature retention loss function is used to guide the training process and maintain the texture structure of the face in the image and strengthen facial features. It can avoid phenomena such as exposure and loss of facial detail information, and obtain clearer facial images. The RetinaFace face detection framework is used to detect the enhanced facial features. It uses a feature pyramid structure and a single stage detection mode to detect facial images. It improves the capability to detect small-scale faces without increasing computational complexity. The experimental results on the public low illumination face dataset DARK FACE and the self built coal mine underground face dataset show that this method improves image contrast, clearly restores facial features in the image, and performs well in accuracy, recall, and average accuracy, effectively improving the accuracy of coal mine underground face detection.

Underground lighting in coal mines is limited. There is a large amount of dust and mist, resulting in low contrast, uneven lighting, weak detail information, and a large amount of noise in the collected images. The image enhancement methods based on traditional models have poor robustness, often causing excessive image enhancement and color distortion. Most image enhancement methods based on deep learning do not consider the noise amplification caused by enhancement. In order to solve the above problems, an enhancement method for low light images in coal mines is proposed. The image enhancement network is constructed by using convolutional neural networks. The network includes feature extraction modules, enhancement modules, and fusion modules. The feature extraction module convolves the input image to varying degrees, extracts multi-level image features, and obtains multiple feature layers. The enhancement module enhances the extracted feature layers through sub-networks to enhance different levels of detail features. The fusion module fuses the enhanced feature layers and outputs enhanced images. Then, through the constraints of the structure loss function, content loss function and area loss function, the image quality is improved. The image color distortion and noise amplification are effectively suppressed to obtain the final enhanced image. The experimental results show that this method can effectively improve the brightness and contrast of low light images in coal mines. The method has strong noise suppression capability, enabling the image to better restore the original details while avoiding overexposure or color distortion.

The electrocatalytic oxidation of urea combined with wastewater splitting is considered a promising approach for sustainable hydrogen production, characterized by minimal energy consumption. However, its evolution is greatly hindered by the shortage of efficient and easily accessible electrocatalytic materials. Here, a facile electrochemical activation strategy was conceived and proposed to construct a Cu-doped NiOOH nanolayer encapsulated on Cu2O nanodendrites on Cu mesh substrate (Cu-NiOOH/Cu2O/CM) from the electrodeposited Ni/Cu2O/CM heterostructured precatalyst. It was verified that the incorporation of Cu not only facilitates the rapid formation of Ni(III) species but also contributes to the formation of Cu-Ni(III) bifunctional electrocatalytic active sites. Benefiting from the accessible Cu-Ni(III) dual active sites, high active surface area, good hydrophilic and aerophobic surface properties and superior electrical conductivity of the Cu mesh substrate, the as-prepared Cu-NiOOH/Cu2O/CM exhibits enhanced bifunctional electrocatalytic abilities for electrocatalytic urea oxidation reaction (UOR) and hydrogen evolution reaction (HER). Particularly, for the Cu-NiOOH/Cu2O/CM||Cu-NiOOH/Cu2O/CM configuration toward the UOR||HER coupled system, a significantly reduced cell voltage of 1.43 V vs. RHE @ 10 mA·cm−2 was obtained. The observed cell voltage for the conventional overall water splitting is approximately 190 mV higher than that observed for overall urea splitting. This study proposes a viable approach to achieve and optimize the bifunctional UOR/HER performance of NiOOH active species, which holds significant importance for efficient and stable hydrogen generation from urea-contaminated substandard water. [ABSTRACT FROM AUTHOR]