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A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

Prostate cancer (PC) is a heterogeneous disease characterized by variable morphological patterns. Thus, establishing a patient-derived xenograft (PDX) model that retains the key features of the primary tumor for each type of PC is important for appropriate evaluation. In this study, we established PDX models of hormonenaïve (D17225) and castration-resistant (B45354) PC by implanting fresh tumor samples, obtained from patients with advanced PC under the renal capsule of immune-compromised mice. Supplementation with exogenous androgens shortened the latent period of tumorigenesis and increased the tumor formation rate. The PDX models exhibited the same major genomic and phenotypic features of the disease in humans and maintained the main pathological features of the primary tumors. Moreover, both PDX models showed different outcomes after castration or docetaxel treatment. The hormone-naïve D17225 PDX model displayed a range of responses from complete tumor regression to overt tumor progression, and the development of castrate-resistant PC was induced after castration. The responses of the two PDX models to androgen deprivation and docetaxel were similar to those observed in patients with advanced PC. These new preclinical PC models will facilitate research on the mechanisms underlying treatment response and resistance.

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[18F]FDG as a probe of PET/CT is a radiolabeled glucose analogue taken up by most cells, but its batch activity is limited. [68Ga]FAPI-04 is a promising alternative based on a fibroblast activation protein-specific inhibitor (FAPI) labeled with radiotracer FAP. Here, a series of databases suggested that FAP expression was significantly different in pancreatic cancer compared to normal tissue. The FAP-positive fibroblasts were evaluated around the tumor cells and the stroma. A patient-derived orthotopic xenograft (PDOX) model of pancreatic adenocarcinoma (PDAC) exhibits significantly higher quantitative uptake of [68Ga]FAPI-04 (P < 0.05) than [18F]FDG PET/CT in various organs. Because of relatively high (T/M) ratios, the [68Ga]FAPI-04 is excellent for B-mode ultrasound, NIRF, and PET/CT. Thus, [68Ga]FAPI-04 PET displayed a better tumor specificity and can be a potential application for the early detection of pancreatic cancer.