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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.

Obesity has become a global public health problem, and its relationship with gastrointestinal diseases has become a major concern. The visceral adiposity index (VAI) is a novel index to assess the distribution and content of visceral fat, and this study aimed to investigate the association between VAI and bowel habits (chronic diarrhea, chronic constipation) and inflammatory bowel disease (IBD). The 2005–2010 National Health and Nutrition Examination Survey (NHANES) dataset was used for the cross-sectional survey. Bowel habits and IBD were defined by self-report. Multiple logistic regression models were used to test the linear association of VAI with bowel habits and IBD. Fitted smoothed curves and threshold effects analyses were used to characterize nonlinear relationships. This cross-sectional study included 10,391 adults (≥ 20 years). After adjusting for covariates, there was a significant negative association between VAI and chronic constipation (OR [95% CI]: 0.97 [0.95, 1.00]) but no significant association with IBD (OR [95% CI]: 0.97 [0.87, 1.07]). Additionally, there was a nonlinear association between VAI and chronic diarrhea with a breakpoint of 3.08, with a positive correlation between the two on the left side of the breakpoint and no statistical significance on the right side. Subgroup analyses and interaction tests showed that maintaining sleep health was associated with a low risk of chronic constipation. Elevated VAI levels were negatively associated with chronic constipation, and elevated levels were positively associated with chronic diarrhea at VAI < 3.08. This reminds us that maintaining moderate levels of visceral fat may prevent the onset of chronic constipation and circumvent the risk of chronic diarrhea. Notably, maintaining healthy sleep may play a positive role in reducing chronic constipation. [ABSTRACT FROM AUTHOR]

Coal gangue, a byproduct of coal mining with massive generation, presents a major challenge for resource utilization. In this study, SiO2 aerogel was successfully fabricated from coal gangue, via a facile and cost-effective approach. The aerogel boasts an intricate network framework structure internally and functions as a mesoporous material with measurements of 154.5662 m2·g−1 for specific surface area and 7.9557 nm for average pore size. Additionally, exploring the adsorption capabilities of SiO2 aerogel concerning Pb(II) in wastewater, the research revealed insights. The results demonstrated that the maximum Pb(II) removal efficiency reached 94.09% under pH 6, aerogel dosage of 1.5 g·L⁻1, and initial Pb(II) concentration of 80 mg·L⁻1. The adsorption process followed the Langmuir isotherm model and pseudo-second-order kinetic model, suggesting a favorable and predominantly monomolecular layer chemisorption, and the intraparticle diffusion model suggests internal diffusion behavior during adsorption. The SiO2 aerogel exhibited specific adsorption behavior towards Pb(II). This study offers a promising avenue for repurposing coal gangue while addressing the issue of Pb(II)-contaminated wastewater. [ABSTRACT FROM AUTHOR]

Urban trees are known for their ability to settle fine particulate matter (PM2.5), yet the effects of historical pollution exposure on their dust-retention capacity and stress memory remain underexplored. Therefore, we selected Euonymus japonicus Thunb. var. aurea-marginatus Hort. and Photinia × fraseri Dress, which are two common urban greening tree species in the Yangtze River Delta, a highly urbanized region in China facing severe air pollution challenges, characterized by dense urban forests, and we employed an aerosol generator to perform controlled experiments aiming to simulate PM2.5 pollution exposure in a sealed chamber. The experiments encompassed a first pollution treatment period P1 (15 days), a recovery period R (15 days), and a second pollution treatment period P2 (15 days). The study investigates the historical impacts of pollution exposure by simulating controlled environmental conditions and assessing the morphological and physiological changes in trees. The main results are as follows: Vd of Euonymus japonicus Thunb. var. aurea-marginatus Hort. significantly decreased on the 10th day during P2 compared with that on the same day during P1, whereas Vd of Photinia × fraseri Dress significantly decreased on the 15th day. Compared with those during P1, the specific leaf area of both plants significantly decreased, the specific leaf weight significantly increased, the wax layer significantly thickened, the stomata decreased, and the content of photosynthetic pigments remained stable during P2. Furthermore, the air pollution tolerance index (APTI) generally increased during both P1 and P2. This study contributes to international knowledge by examining stress memory in urban trees and underscores the role of stress memory in enhancing plant resistance to periodic particulate pollution, offering insights into the adaptive mechanisms that can be applied globally, not just regionally. [ABSTRACT FROM AUTHOR]

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]