Pulsed thermography is a contactless and rapid non-destructive evaluation (NDE) technique that is widely used for the inspection of fibre reinforced plastic composites. However, pulsed thermography uses expensive and specialist equipment such high-energy flash lamps to generate heat into the sample, so that alternative thermal stimulation sources are needed. Long pulse thermography was recently developed as a cost-effective solution to enhance the defect detectability in composites by generating step-pulse heat into the test sample with inexpensive quartz halogen lamps and measuring the thermal response during the material cooling down. This paper provides a quantitative comparison of long pulse thermography with traditional pulsed thermography and step heating thermography in carbon fibre and glass fibre composites with flat-bottomed holes located at various depths. The three thermographic methods are processed with advanced thermal image algorithms such as absolute thermal contrast, thermographic signal reconstruction, phase Fourier analysis and principal component analysis in order to reduce thermal image artefacts. Experimental tests have shown that principal component analysis applied to long pulse thermography provides accurate imaging results over traditional pulsed thermography and step heating thermography. Hence, this inspection technique can be considered as an efficient and cost-effective thermographic method for low thermal conductivity and low thermal response rate materials.
Background: UBASH3B (STS1) is an important gene that negatively regulates T-cell receptor signaling in activated T-lymphocytes that involved in cancers. However, the function of UBASH3B in prostate cancer (PCa) and the correlation between UBASH3B and tumor-infiltrating immune cells still remain unclear. Methods: Real-time PCR and immunohistochemistry were applied to detect mRNA and protein expression of UBASH3B in PCa patients and benign prostate hyperplasia patients (BPH). Clinical features of patients with PCa were recorded and Kaplan Meier curve was subsequently plotted. Based on mRNA expression of UBASH3B, patients with PCa from TCGA database were divided into low-UBASH3B-expression group and high-UBASH3B-expression group for construct lncRNA-miRNA-mRNA network and analyzing GO and KEGG pathways. Single gene analysis method was performed by using GSEA to interpret gene expression data in PCa. The PPI network was constructed using STRING and the correlation between UBASH3B and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT. Results: The mRNA and protein expression of UBASH3B were upregulated in PCa. The abundant expression of UBASH3B is associated with poor prognosis in PCa. The subnetwork of UBASH3B contains three lncRNAs (MIAT, LINC01297, MYLK-AS1) and four miRNAs (hsa-miR-200a-3p, hsa-miR-455-5p, hsa-miR-192-5p, hsamiR-215-5P). The mRNA expression of UBASH3B was involved in 28 KEGG pathways. GSEA analysis showed that 18 hallmark gene sets were significantly enriched in high-UBASH3B-expression, whereas 1 gene set was enriched in low-UBASH3B-expression. PPI network revealed a tightly interaction between UBASH3B and LCP2 (an immune related gene). TIMER and CIBERSORT database indicated that UBASH3B was correlated with 11 types of tumor-infiltrating immune cells (naïve B cell, memory B cells, resting CD4 + memory T cell, activated CD4 + memory T cell, regulatory T cell, activated NK cell, M2 macrophages, resting dendritic cells, activated dendritic cells, resting mast cells, neutrophils). Conclusions: In conclusion, UBASH3B may be a novel potential prognostic biomarker and is associated with tumor-infiltrating immune cells in tumor microenvironment, suggesting UBASH3B as a potential target for future treatment of PCa.
Background: The aim of this study was to determine whether the indicators obtained from intravoxel incoherent motion (IVIM) imaging can improve the characterization of benign and malignant breast masses compared with conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI). Patients and Methods: This study included 23 benign and 31 malignant breast masses of 48 patients. Main indicators were initial enhancement ratio (IER), time-signal intensity curve (TIC), apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). The discriminative abilities of the different models were compared by means of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) analysis. Results: D had the highest AUC (0.980), sensitivity (93.55%), specificity (100%), and diagnostic accuracy (96.36%). Both D and TIC could provide the independent predicted features for malignant breast masses. The combination of D and TIC had an AUC of up to 0.990. Conclusion: D of IVIM can effectively complement existing conventional DCE-MRI and DW-MRI in differentiating malignant from benign breast masses. IVIM combined with DCE-MRI is a robust means of evaluating breast masses.
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