To better treat patients with non–small cell lung cancer (NSCLC), the investigations on novel molecules affecting NSCLC progression are of vital importance. Long noncoding RNAs (lncRNAs) are identified as pivotal regulators that can affect the cellular activities of carcinomas. Long intergenic non‐protein coding RNA 667 (LINC00667) is a newly found lncRNA, and its expression pattern and potent mechanisms are still obscure in NSCLC. Our study was the first to illustrate that LINC00667 was upregulated in NSCLC and LINC00667 silence refrained the proliferation, migration, and angiogenesis of NSCLC cells in vitro. In addition, vascular endothelial growth factor A (VEGFA) was modulated by LINC00667 at posttranscriptional level. Furthermore, mechanism experiments depicted that LINC00667 recruited eukaryotic translation initiation factor 4A3 (EIF4A3) to stabilize VEGFA messenger RNA. Eventually, rescue assays implied that LINC00667 modulated NSCLC progression via EIF4A3‐stabilized VEGFA. Jointly, these findings hinted that LINC00667 was a tumor promoter in NSCLC, providing guidance for the exploration on NSCLC treatment.
A model-based wavefront sensorless (WFSless) adaptive optics (AO) system with a 61-element deformable mirror is simulated to correct the imaging of a turbulence-degraded extended object. A fast closed-loop control algorithm, which is based on the linear relation between the mean square of the aberration gradients and the second moment of the image intensity distribution, is used to generate the control signals for the actuators of the deformable mirror (DM). The restoration capability and the convergence rate of the AO system are investigated with different turbulence strength wave-front aberrations. Simulation results show the model-based WFSless AO system can restore those images degraded by different turbulence strengths successfully and obtain the correction very close to the achievable capability of the given DM. Compared with the ideal correction of 61-element DM, the averaged relative error of RMS value is 6%. The convergence rate of AO system is independent of the turbulence strength and only depends on the number of actuators of DM.
BackgroundLung adenocarcinoma (LUAD) is one of the most predominant subtypes of lung cancer. The gut microbiome plays a vital role in the pathophysiological processes of various diseases, including cancers.MethodsIn the study, 100 individuals were enrolled. In total 75 stool and blood samples were analyzed with 16s-rRNA gene sequencing and metabolomics (30 from healthy individuals (H); 45 from LUAD patients). In addition, 25 stool samples were analyzed with metagenomics (10 from H; 15 from LUAD). The linear discriminant analysis (LDA) effect size (LefSe) and logistic regression analysis were applied to identify biomarkers’ taxa and develop a diagnostic model. The diagnostic power of the model was estimated with the receiver operating characteristic curve (ROC) by comparing the area under the ROC (AUC). The correlation between biomarker’s taxa and metabolites was calculated using the Spearman analysis.ResultsThe α and β diversity demonstrated the composition and structure of the gut microbiome in LUAD patients were different from those in healthy people. The top three abundance of genera were Bacteroides (25.06%), Faecalibacterium (11.00%), and Prevotella (5.94%). The LefSe and logistic regression analysis identified three biomarker taxa (Bacteroides, Pseudomonas, and Ruminococcus gnavus group) and constructed a diagnostic model. The AUCs of the diagnostic model in 16s-rRNA gene sequencing and metagenomics were 0.852 and 0.841, respectively. A total of 102 plasma metabolites were highly related to those three biomarkers’ taxa. Seven metabolic pathways were enriched by 102 plasma metabolites, including the Pentose phosphate pathway, Glutathione metabolism.ConclusionsIn LUAD patients, the gut microbiome profile has significantly changed. We used three biomarkers taxa to develop a diagnostic model, which was accurate and suitable for the diagnosis of LUAD. Gut microbes, especially those three biomarkers’ taxa, may participate in regulating metabolism-related pathways in LUAD patients, such as the pentose phosphate pathway and glutathione metabolism.
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