Abstract Evidence has shown a strong relationship between smoking and epithelial mesenchymal transition (EMT). α5‐nicotinic acetylcholine receptor (α5‐nAChR) contributes to nicotine‐induced lung cancer cell EMT. The cytoskeleton‐associated protein PLEK2 is mainly involved in cytoskeletal protein recombination and cell stretch migration regulation, which is closely related to EMT. However, little is known about the link between nicotine/α5‐nAChR and PLEK2 in lung adenocarcinoma (LUAD). Here, we identified a link between α5‐nAChR and PLEK2 in LUAD. α5‐nAChR expression was correlated with PLEK2 expression, smoking status and lower survival in vivo. α5‐nAChR mediated nicotine‐induced PLEK2 expression via STAT3. α5‐nAChR/PLEK2 signaling is involved in LUAD cell migration, invasion and stemness. Moreover, PLEK2 was found to interact with CFL1 in nicotine‐induced EMT in LUAD cells. Furthermore, the functional link among α5‐nAChR, PLEK2 and CFL1 was confirmed in mouse xenograft tissues and human LUAD tissues. These findings reveal a novel α5‐nAChR/PLEK2/CFL1 pathway involved in nicotine‐induced LUAD progression.
The CHRNΑ5 gene, which encodes the α5-nicotinic acetylcholine receptor (α5-nAChR), is related to lung cancer and nicotine addiction. Smoking is closely related to the immunosuppressive effect of macrophages. CD47, a phagocytosis checkpoint in macrophages, is a therapeutic target in various cancer types. Nevertheless, the relationship between α5-nAChR and CD47 in lung cancer is still unclear.
With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.
Abstract Background Immunotherapy has proven to be an emerging treatment for non-small-cell lung cancer in recent years. Notably, smokers show higher programmed cell death ligand-1 (PD-L1) expression and better responses to PD-1/PD-L1 inhibitors than nonsmokers. Genome-wide association studies show that the CHRNΑ5 encoding α5-nicotinic acetylcholine receptor (α5-nAChR) is especially relevant to lung cancer and nicotine dependence. Jab1 is a key regulatory factor and promotes the stabilization of PD-L1. Our previous study reported that α5-nAChR mediates lung adenocarcinoma (LUAD) epithelial-mesenchymal transition (EMT) and metastasis via STAT3/Jab1. However, the link between α5-nAChR and PD-L1 is unclear in LUAD. Methods We used various bioinformatics databases to analyze the expression of related genes and their correlations. Expression and clinicopathologic significance of α5-nAChR and PD-L1 were detected by immunohistochemistry in a tissue microarray. α5-nAChR regulated LUAD cell immune escape by targeting the STAT3/Jab1-PD-L1 signalling by Western-blotting and ChIP in vitro. We used T cell coculture, flow cytometry, ELISA, CCK8 assay and crystal violet staining to detect the expression of regulatory T cell (Tregs), IFN-γ, IL-2 and the ability of T cell-mediated tumour cell killing respectively. IF assays were performed in both cancer cells and tumour xenograft paraffin sections to analyze the protein expression. The in vivo experiments in mouse model were performed to show the α5-nAChR-mediated immune escape via PD-L1 pathway. Results The expression of α5-nAChR was correlated with PD-L1 expression, smoking status and lower survival of LUAD in vivo. In vitro, the expression of α5-nAChR mediated phosphorylated STAT3 (pSTAT3), Jab1 and PD-L1 expression. STAT3 bound to the Jab1 or PD-L1 promoter and mediated PD-L1 expression. Jab1 stabilized PD-L1 expression in LUAD cells. Furthermore, in primary T cell cocultured system, downregulation of α5-nAChR suppressed the function of CD4 + CD25 + FOXP3 + Tregs, enhanced IFN-γ secretion, and increased T cell-mediated killing of LUAD cells. In the Jurkat T cells and LUAD cells coculture assay, inhibition of α5-nAChR increased IL-2 secretion. In tumour xenograft tissues, α5-nAChR expression was related to PD-L1, Jab1, pSTAT3, CD4 and granzyme B expression (GB). Conclusions Our results suggest that the novel α5-nAChR/STAT3-Jab1-PD-L1 axis is involved in LUAD immune escape, which could lead to potential therapeutic strategies for cancer immunotherapy.
Abstract Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources.
Photoacoustic microscopy (PAM) leverages the photoacoustic effect to provide high-resolution structural and functional imaging. However, achieving high-speed imaging with high spatial resolution remains challenging. To address this, undersampling and deep learning have emerged as common techniques to enhance imaging speed. Yet, existing methods rarely achieve effective recovery of functional images. In this study, we propose Mask-enhanced U-net (MeU-net) for recovering sparsely sampled PAM structural and functional images. The model utilizes dual-channel input, processing photoacoustic data from 532 nm and 558 nm wavelengths. Additionally, we introduce an adaptive vascular attention mask module that focuses on vascular information recovery and design a vessel-specific loss function to enhance restoration accuracy. We simulate data from mouse brain and ear imaging under various levels of sparsity (4 ×, 8 ×, 12 ×) and conduct extensive experiments. The results demonstrate that MeU-net significantly outperforms traditional interpolation methods and other representative models in structural information and oxygen saturation recovery.
α5-nicotinic acetylcholine receptor (α5-nAChR) plays a vital part in lung adenocarcinoma (LUAD). However, it is not comprehensively understood that how the α5-nAChR affects LUAD. Through diverse bioinformatics analyses and immunohistochemistry, the expressions of α5-nAChR and SOX2 as well as their relations were dissected. α5-nAChR regulated the differentiation of monocytes into M2 macrophages by targeting the STAT3/SOX2/CSF-1 signaling in the coculture system by western blotting and ChIP. α5-nAChR-mediated macrophage-mediated LUAD cell migration via SOX2/CSF-1 signaling in the cocultured medium. Correlations of α5-nAChR, SOX2 and M2 phenotype tumor-associated macrophages (TAMs) were validated in mouse LUAD models and clinical samples. α5-nAChR expression was connected to SOX2 expression, smoking and bad prognosis of LUAD among clinical samples. Nicotine-induced SOX2 expression was mediated by α5-nAChR via STAT3. Additionally, SOX2-mediated macrophage colony-stimulating factor (CSF-1) expression contributed to LUAD progression in vitro. Furthermore, α5-nAChR expression was strongly linked to pSTAT3, SOX2 and M2 macrophage marker CD206 expression and negatively correlated with M1 macrophage marker CD86 expression in vivo. It is indicated that M2 macrophages are mediated by the new α5-nAChR /SOX2/CSF-1 axis in nicotine-related LUAD, which is a potential therapeutic strategy for cancer.
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