Search

Background: Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. Methods: We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. Results: 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573–0.853), specificity of 0.869 (95% CI 0.764–0.974), F1 of 0.699 (95% CI 0.557–0.841), and AUC of 0.864 (95% CI 0.794–0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. Conclusion: Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients. [ABSTRACT FROM AUTHOR]

Background: Neonatal lesion in the ventral hippocampus (NLVH) is a validated animal model to study schizophrenia from a neurodevelopmental perspective. This animal model is also used to investigate how neonatal lesions may alter the genetic expression of dopaminergic receptors. The present study compares mRNA expression levels of dopamine receptors (drd2 and drd3) in lymphocytes and brain of NLVH animals at two different age stages: young and adult. Methods: The NLVH procedure was performed on 20 male Wistar rats at postnatal days 5-7. The mRNA expression levels of drd2 and drd3 genes in lymphocytes, nucleus accumbens, hippocampus and prefrontal cortex were measured and analyzed at postnatal days 45 and 90. The results were compared and contrasted with respective sham groups. Results: In lymphocytes, only in NLVH-adult group we observed drd2 mRNA expression, while drd2 mRNA expression was not observed in the NLVH-juvenile rats; on the other hand, the drd3 mRNA expression did not show significant statistical differences. In hippocampus no differences were observed between drd2 mRNA or drd3 mRNA expression when comparing juvenile/adult shams with NLVH groups. In the prefrontal area, a decrease in drd2 mRNA expression levels were observed in the NLVH-adult group (F(1,3) = 52.83, p = 0,005) in comparison to the sham-adult group. Finally, in the nucleus accumbens, a strong decrease of drd3 mRNA expression was observed in the NLVH-adult group in comparison to the sham-adult group (F(1,3) = 123,2, p < 0.001). Conclusions: Our results show that differences in drd2 and drd3 mRNA levels in NLVH-adults are patent when compared to the sham-adult group or with the NLVH-juvenile group. These findings suggest that the expression levels may be regulated during adulthood, leading to behavioral and neurochemical changes related to schizophrenia. Therefore, more studies are necessary to determine the role of dopamine receptors as possible molecular markers for neurodevelopmental changes associated with schizophrenia. [ABSTRACT FROM AUTHOR]