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• Clinicopathologic features in melanoma patients have been related to CXCR4 expression. • High expression of CXCR4 can mediate tumor thickness and ulceration in melanoma patients. • The CXCL12/CXCR4 axis promotes metastasis to the specific site of the body. • Invasion of melanoma cells to lymph nodes is dependent on the CXCR4 expression. CXCR4 is a member of CXC-type and G protein-coupled receptors that can conduce many biological processes, including hemostasis, migration, and adhesion of different types of immune cells. Also, the contribution of CXCR4 in metastasis cascade and development of various malignancies has been addressed in previous reports. This meta -analysis was performed to explore whether the CXCR4 expression affects prognosis and clinicopathologic features in melanoma cancer. Our study involved 656 melanoma patients from 13 reports by detailed literature search from PubMed, Embase, Web of Science, and Google Scholar up to April 2021. To evaluate the association between CXCR4 expression and clinicopathological features of melanoma, we calculated odds ratios (ORs) with its 95% confidence intervals (CIs). We indicated that the CXCR4 overexpression was obviously correlated with ulceration (OR = 0.56, 95% CI: 0.38 to 0.74; I2 = 0.0%, P = 0.999), tumor thickness (OR = 0.56, 95% CI: 0.38 to 0.74; I2 = 0.0%, P = 0.999) and lymph node metastasis (OR = 8.54, 95% CI: 1.04 to 16.04; I2 = 98.9, P < 0.0001). In conclusion, our results reveal that CXCR4 is involved in enhancing the progression and metastasis of melanoma, and further clinical studies are necessary to investigate the role of CXCR4 as a diagnostic and therapeutic biomarker through the progress of melanoma cancer. [ABSTRACT FROM AUTHOR]

We present methods for turning pair-wise registration algorithms into drift-free trackers. Such registration algorithms are abundant, but the simplest techniques for building trackers on top of them exhibit either limited tracking range or drift. Our algorithms maintain the poses associated with a number of key frames, building a view-based appearance model that is used for tracking and refined during tracking. The first method we propose is batch oriented and is ideal for offline tracking. The second is suited for recovering egomotion in large environments where the trajectory of the camera rarely intersects itself, and in other situations where many views are necessary to capture the appearance of the scene. The third method is suitable for situations where a few views are sufficient to capture the appearance of the scene, such as object-tracking. We demonstrate the techniques on egomotion and head-tracking examples and show that they can track for an indefinite amount of time without accumulating drift. [Copyright &y& Elsevier]