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Label-free Fluorescence Switch on Trypsin Assay Determined by Gemini Surfactant/heparin/Nile Reddish Supramolecular Assembly.
Background Accurate segmentation of tumor targets is critical for maximizing tumor control and minimizing normal tissue toxicity. We proposed a sequential and iterative U-Net (SI-Net) deep learning method to auto-segment the high-risk primary tumor clinical target volume (CTVp1) for treatment planning of nasopharyngeal carcinoma (NPC) radiotherapy. Methods The SI-Net is a variant of the U-Net architecture. The input of SI-Net includes one CT image, the CTVp1 contour on this image, and the next CT image. The output is the predicted CTVp1 contour on the next CT image. We designed the SI-Net, using the left side to learn the volumetric features and the right to localize the contour on the next image. Two prediction directions, one from inferior to superior (forward direction) and the other from superior to inferior (backward direction), were tested. The performance was compared between the SI-Net and the U-Net using Dice similarity coefficient (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD) metrics. Results The DSC and JI values from the forward direction SI-Net model were 5 and 6% higher than those from the U-Net model (0.84 ± 0.04 vs. 0.80 ± 0.05 and 0.74 ± 0.05 vs. 0.69 ± 0.05, p less then 0.001). The smaller ASD and HD values also indicated a better performance (2.8 ± 1.0 vs. 3.3 ± 1.0 mm and 8.7 ± 2.5 vs. 9.7 ± 2.7 mm, p less then 0.01) for the SI-Net model. For the backward direction SI-Net model, the DSC and JI values were still better than those from the U-Net model (p less then 0.01), although there were no significant differences in ASD and HD. Conclusions The SI-Net model preserved the continuity between adjacent images and thus improved the segmentation accuracy compared with the conventional U-Net model. This model has potential of improving the efficiency and consistence of CTVp1 contouring for NPC patients.Activated Cdc42-associated kinase1 (ACK1), a non-receptor tyrosine kinase, has been considered as an oncogene and therapeutic target in various cancers. However, its contribution to cancer immunity remains uncertain. Here we first compared the profiles of immune cells in cancerous and normal tissues in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Next, we found that the immune cell infiltration levels were associated with the ACK1 gene copy numbers in lung cancer. Consistently, our RNA-seq data unveiled that the silencing of ACK1 upregulated several immune pathways in lung cancer cells, including the T cell receptor signaling pathway. The impacts of ACK1 on immune activity were validated by Gene Set Enrichment Analysis of RNA-seq data of 188 lung cancer cell lines from the public database. A pathway enrichment analysis of 35 ACK1-associated immunomodulators and 50 tightly correlated genes indicated the involvement of the PI3K-Akt and Ras signaling pathways. Based on ACK1-associated immunomodulators, we established multiple-gene risk prediction signatures using the Cox regression model. The resulting risk scores were an independent prognosis predictor in the TCGA lung cohorts. We also accessed the prognostic accuracy of the risk scores with a receiver operating characteristic methodology. Finally, a prognostic nomogram, accompanied by a calibration curve, was constructed to predict individuals' 3- and 5-year survival probabilities. https://www.selleckchem.com/products/atglistatin.html Our findings provided evidence of ACK1's implication in tumor immunity, suggesting that ACK1 may be a potential immunotherapeutic target for non-small cell lung cancer (NSCLC). The nominated immune signature is a promising prognostic biomarker in NSCLC.Theranostics are nano-size or molecular-level agents serving for both diagnosis and therapy. Structurally, they are drug delivery systems integrated with molecular or targeted imaging agents. Theranostics are becoming popular because they are targeted therapeutics and can be used with no or minimal changes for diagnostic imaging to aid in precision medicine. Thus, there is a close relation between theranostics and image-guided therapy (IGT), and theranostics are actually a subclass of IGT in which both therapeutic and imaging functionalities are attributed to a single platform. An important theranostics strategy is biological pretargeting. In pretargeted IGT, first, the target is identified by a target-specific natural or synthetic bioligand followed by a nano-scale or molecular drug delivery component, which form therapeutic clusters by in situ conjugation reactions. If pretargeted drug delivery platforms are labeled with multimodal imaging probes, they can be used as theranostics for both diagnostic imaging and therapy. Optical and nuclear imaging techniques have mostly been used in proof-of-concept studies with pretargeted theranostics. The concept of pretargeting in theranostics is comparatively novel and generally requires a confirmed overexpression of surface receptors on targeted cells/tissue. In addition, the receptors should have natural or synthetic bioligands to be used as pretargeting components. Therefore, applications of pretargeting theranostics are still limited to several cancer types, which overexpress cell-surface markers on the target cancer cells. In this review, recent discoveries of pretargeting theranostics in breast, ovarian, prostate, and colorectal cancers are discussed to highlight main strengths and potential limitations the strategy.Objective TNFAIP2 is a novel gene induced by TNF-α and participates in inflammatory reaction and tumor angiogenesis. This study aims to understand the correlation between TNFAIP2 gene polymorphism and prediction as well as prognosis of gastric cancer (GC) in a Chinese population. Methods One thousand two hundred seventy-nine cases were enrolled, including 640 GC and 639 non-cancer cases. The functional tagSNPs of the TNFAIP2 gene were screened by Haploview software and NIH Snpinfo website. Human whole-blood genomic DNA was extracted by phenol chloroform method and analyzed by KASP SNP typing and sequencing method. ELISA was used to determine the expression of TNFAIP2 protein in serum samples. The miRNAs bound to TNFAIP2 3' UTR rs8126 were predicted by MirSNP and TargetScan database. SPSS 22.0 software was used for statistical analysis, and P C polymorphism might affect TNFAIP2 protein expression.
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