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The pore structure of shale oil reservoirs is complex, and the microscale and nanoscale effect is obvious in the development of shale oil reservoirs. Understanding the oil flow mechanism in shale reservoirs is essential for optimizing the development plan and enhancing the recovery rate of shale oil reservoirs. In this review, we briefly introduce the occurrence status of shale oil and shale oil flow in the inorganic matrix and the organic matrix (including the shrinkage of kerogen, oil diffusion in kerogen, oil transport in the organic pore channels, coupling of diffusion, and fluid transport in the organic matrix). Then, the shale oil microflow simulation and a coupling model of double-porous media for microflow and macroflow in the production process of shale oil are discussed. Finally, we summarize the main conclusions and perspectives on the oil flow mechanism and numerical simulations in shale oil reservoirs. An accurate description of shale oil occurrence status and shale oil flow in the inorganic and organic matrices is crucial for the numerical simulation of shale oil reservoirs. It can provide a basis and reference for the future directions of shale oil flow and numerical simulations during the development of shale oil reservoirs. [ABSTRACT FROM AUTHOR]

PSA screening reduces PCa-mortality but the disadvantages overdiagnosis and overtreatment require multivariable risk-prediction tools to select appropriate treatment or active surveillance. This review explains the differences between the two largest screening trials and discusses the drawbacks of screening and its meta-analysisxs. The current American and European screening strategies are described. Nonetheless, PSA is one of the most widely used tumor markers and strongly correlates with the risk of harboring PCa. However, while PSA has limitations for PCa detection with its low specificity there are several potential biomarkers presented in this review with utility for PCa currently being studied. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved prostate health index (phi) shows improved specificity over percent free and total PSA. Another kallikrein panel, 4K, which includes KLK2 has recently shown promise in clinical research studies but has not yet undergone formal validation studies. In urine, prostate cancer gene 3 (PCA3) has also been validated and approved by the FDA for its utility to detect PCa. The potential correlation of PCA3 with cancer aggressiveness requires more clinical studies. The detection of the fusion of androgen-regulated genes with genes of the regulatory transcription factors in tissue of (~)50% of all PCa-patients is a milestone in PCa research. A combination of the urinary assays for TMPRSS2:ERG gene fusion and PCA3 shows an improved accuracy for PCa detection. Overall, the field of PCa biomarker discovery is very exciting and prospective.

Background: We compared urinary prostate cancer antigen 3 (PCA3), transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG), and the serum [-2]proprostate-specific antigen ([-2]proPSA)-based prostate health index (Phi) for predicting biopsy outcome., Methods: Serum samples and first-catch urine samples were collected after digital rectal examination (DRE) from consented outpatients with PSA 0.5-20 μg/L who were scheduled for prostate biopsy. The PCA3 score (PROGENSA PCA3, Hologic Gen-Probe) and T2:ERG score (Hologic Gen-Probe) were determined. Measurements of serum PSA, free PSA, and [-2]proPSA (Beckman Coulter) were performed, and the percentages of free PSA (%fPSA) and Phi ([-2]proPSA/fPSA × √PSA) were determined., Results: Of 246 enrolled men, prostate cancer (PCa) was diagnosed in 110 (45%) and there was no evidence of malignancy (NEM) in 136 (55%). A first set of biopsies was performed in 136 (55%) of all men, and 110 (45%) had ≥1 repeat biopsies. PCA3, Phi, and T2:ERG differed significantly between men with PCa and NEM, and these markers showed the largest areas under the ROC curve (AUCs) (0.74, 0.68, and 0.63, respectively). PCA3 had the largest AUC of all parameters, albeit not statistically different from Phi. Phi showed somewhat lower specificities than PCA3 at 90% sensitivity. Combination of both markers enhanced diagnostic power with modest AUC gains of 0.01-0.04. Although PCA3 had the highest AUC in the repeat-biopsy cohort, the highest AUC for Phi was observed in DRE-negative patients with PSA in the 2-10 μg/L range., Conclusions: PCA3 and Phi were superior to the other evaluated parameters but their combination gave only moderate enhancements in diagnostic accuracy for PCa at first or repeat prostate biopsy., (© 2012 American Association for Clinical Chemistry)