Prostate Cancer: Advances in Genetic Testing and Clinical Implications

Abdelrazek, Ahmad; Ghoniem, Khaled; Ahmed, Mohamed E.; Joshi, Vidhu; Mahmoud, Ahmed; Saeed, Nader; Khater, Nazih; ElSharkawy, Mohammed Said; Gamal, Ahmed; Kwon, Eugene D.; Kendi, Ayse T.

doi:10.3390/uro3020012

Cited by 2 publications

(1 citation statement)

References 97 publications

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“…Localized on chromosome 17, Homeobox B13 (HOXB13) is a homeobox transcription factor with mutations being reported in 0.7% to 1.4% of PCa cases [216]. The homeobox genes encode for nuclear proteins and possess the main function of being transcription factors.…”

Section: Homeobox B13mentioning

confidence: 99%

Decoding the Influence of Obesity on Prostate Cancer and Its Transgenerational Impact

Santos-Pereira,

Pereira,

Rebelo

et al. 2023

Nutrients

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In recent decades, the escalating prevalence of metabolic disorders, notably obesity and being overweight, has emerged as a pressing concern in public health. Projections for the future indicate a continual upward trajectory in obesity rates, primarily attributable to unhealthy dietary patterns and sedentary lifestyles. The ramifications of obesity extend beyond its visible manifestations, intricately weaving a web of hormonal dysregulation, chronic inflammation, and oxidative stress. This nexus of factors holds particular significance in the context of carcinogenesis, notably in the case of prostate cancer (PCa), which is a pervasive malignancy and a leading cause of mortality among men. A compelling hypothesis arises from the perspective of transgenerational inheritance, wherein genetic and epigenetic imprints associated with obesity may wield influence over the development of PCa. This review proposes a comprehensive exploration of the nuanced mechanisms through which obesity disrupts prostate homeostasis and serves as a catalyst for PCa initiation. Additionally, it delves into the intriguing interplay between the transgenerational transmission of both obesity-related traits and the predisposition to PCa. Drawing insights from a spectrum of sources, ranging from in vitro and animal model research to human studies, this review endeavors to discuss the intricate connections between obesity and PCa. However, the landscape remains partially obscured as the current state of knowledge unveils only fragments of the complex mechanisms linking these phenomena. As research advances, unraveling the associated factors and underlying mechanisms promises to unveil novel avenues for understanding and potentially mitigating the nexus between obesity and the development of PCa.

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Section: Homeobox B13mentioning

confidence: 99%

Decoding the Influence of Obesity on Prostate Cancer and Its Transgenerational Impact

Santos-Pereira,

Pereira,

Rebelo

et al. 2023

Nutrients

View full text Add to dashboard Cite

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Revolutionizing Prostate Whole-Slide Image Super-Resolution: A Comparative Journey from Regression to Generative Adversarial Networks

Gavade,

Gadad,

Gavade

et al. 2024

Uro

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Microscopic and digital whole-slide images (WSIs) often suffer from limited spatial resolution, hindering accurate pathological analysis and cancer diagnosis. Improving the spatial resolution of these pathology images is crucial, as it can enhance the visualization of fine cellular and tissue structures, leading to more reliable and precise cancer detection and diagnosis. This paper presents a comprehensive comparative study on super-resolution (SR) reconstruction techniques for prostate WSI, exploring a range of machine learning, deep learning, and generative adversarial network (GAN) algorithms. The algorithms investigated include regression, sparse learning, principal component analysis, bicubic interpolation, multi-support vector neural networks, an SR convolutional neural network, and an autoencoder, along with advanced SRGAN-based methods. The performance of these algorithms was meticulously evaluated using a suite of metrics, such as the peak signal-to-noise ratio (PSNR), structural similarity index metrics (SSIMs), root-mean-squared error, mean absolute error and mean structural similarity index metrics (MSSIMs). The comprehensive study was conducted on the SICAPv2 prostate WSI dataset. The results demonstrated that the SRGAN algorithm outperformed other algorithms by achieving the highest PSNR value of 26.47, an SSIM of 0.85, and an MSSIM of 0.92, by 4× magnification of the input LR image, preserving the image quality and fine details. Therefore, the application of SRGAN offers a budget-friendly counter to the high-cost challenge of acquiring high-resolution pathology images, enhancing cancer diagnosis accuracy.

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Prostate Cancer: Advances in Genetic Testing and Clinical Implications

Cited by 2 publications

References 97 publications

Decoding the Influence of Obesity on Prostate Cancer and Its Transgenerational Impact

Decoding the Influence of Obesity on Prostate Cancer and Its Transgenerational Impact

Revolutionizing Prostate Whole-Slide Image Super-Resolution: A Comparative Journey from Regression to Generative Adversarial Networks

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