High altitude exposure affects male reproductive parameters: Could it also affect the prostate?†

Living at high altitudes and living with prostatic illness are two different conditions closely related to a hypoxic environment. People at high altitudes exposed to acute, chronic, or intermittent hypobaric hypoxia turn on several mechanisms at the system, cellular and molecular level to cope with oxygen atmosphere scarcity maintaining the oxygen homeostasis. This exposure affects the whole organism and function of many systems, such as cardiovascular, respiratory, and reproductive. On the other hand, malignant prostate is related to the scarcity of oxygen in the tissue microenvironment due to its low availability and high consumption due to the swift cell proliferation rates. Based on the literature, this similarity in the oxygen scarcity suggests that hypobaric hypoxia, and other common factors between these two conditions, could be involved in the aggravation of the pathological prostatic status. However, there is still a lack of evidence in the association of this disease in males at high altitudes. This review aims to examine the possible mechanisms that hypobaric hypoxia might negatively add to the pathological prostate function in males who live and work at high altitudes. More profound investigations of hypobaric hypoxia’s direct action on the prostate could help understand this exposure’s effect and prevent worse prostate illness impact in males at high altitudes.

Prostate Cancer Delineation in MRI Images Based on Deep Learning: Quantitative Comparison and Promising Perspective

Prostate cancer is the most common malignant male tumor. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection, diagnosis, and treatment of prostate cancer diseases. Computer-aided diagnosis systems can help doctors to analyze MRI images and detect prostate cancer earlier. One of the key stages of prostate cancer CAD systems is the automatic delineation of the prostate. Deep learning has recently demonstrated promising segmentation results with medical images. The purpose of this paper is to compare the state-of-the-art of deep learning-based approaches for prostate delineation in MRI images and discussing their limitations and strengths. Besides, we introduce a promising perspective for prostate tumor classification in MRI images. This perspective includes the use of the best segmentation model to detect the prostate tumors in MRI images. Then, we will employ the segmented images to extract the radiomics features that will be used to discriminate benign or malignant prostate tumors.

Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Dietary Phytochemicals in Zinc Homeostasis: A Strategy for Prostate Cancer Management

Studies have suggested an important role of the trace element zinc (Zn) in prostate biology and functions. Zn has been shown to exist in very high concentrations in the healthy prostate and is important for several prostatic functions. In prostate cancer (PCa), Zn levels are significantly decreased and inversely correlated with disease progression. Ideally, restoration of adequate Zn levels in premalignant/malignant prostate cells could abort prostate malignancy. However, studies have shown that Zn supplementation is not an efficient way to significantly increase Zn concentrations in PCa. Based on a limited number of investigations, the reason for the lower levels of Zn in PCa is believed to be the dysregulation of Zn transporters (especially ZIP and ZnT family of proteins), metallothioneins (for storing and releasing Zn), and their regulators (e.g., Zn finger transcription factor RREB1). Interestingly, the level of Zn in cells has been shown to be modulated by naturally occurring dietary phytochemicals. In this review, we discussed the effect of selected phytochemicals (quercetin, resveratrol, epigallocatechin-3-gallate and curcumin) on Zn functioning and proposes that Zn in combination with specific dietary phytochemicals may lead to enhanced Zn bioaccumulation in the prostate, and therefore, may inhibit PCa.

Plasma Deposited Polyoxazoline Films Integration Into Spiral Microfluidics for the Targeted Capture of Size Selected Cells

Biomolecules readily and irreversibly bind to plasma deposited Polyoxazoline thin films in physiological conditions. The unique reactivity of these thin films toward antibodies is driving the development of immunosensing platforms for applications in cancer diagnostics. However, in order for these coatings to be used as advanced immunosensors, they need to be incorporated into microfluidic devices that are sealed via plasma bonding. In this work, the thickness, chemistry and reactivity of the polyoxazoline films were assessed following plasma activation. Films deposited from methyl and isopropenyl oxazoline precursors were integrated into spiral microfluidic devices and biofunctionalized with prostate cancer specific antibodies. Using microbeads as model particles, the design of the spiral microfluidic was optimised to enable the size-based isolation of cancer cells. The device was tested with a mixed cell suspension of healthy and malignant prostate cells. The results showed that, following size-specific separation in the spiral, selective capture was achieved on the immunofunctionalised PPOx surface. This proof of concept study demonstrates that plasma deposited polyoxazoline can be used for immunosensing in plasma bonded microfluidic devices.