Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.
Peroxisome proliferator-activated receptor γ (PPARγ) is a metabolic regulator that plays an important role in sensitizing tissues to the action of insulin and in normalizing serum glucose and free fatty acids in type 2 diabetic patients. The receptor has also been implicated in the modulation of inflammatory responses, and ligands of PPARγ have been found to induce apoptosis in lymphocytes. However, apoptosis induction may not depend on the receptor, because high doses of PPARγ agonists are required for this process. Using cells containing or lacking PPARγ, we reported previously that PPARγ attenuates apoptosis induced by cytokine withdrawal in a murine lymphocytic cell line via a receptor-dependent mechanism. PPARγ exerts this effect by enhancing the ability of cells to maintain their mitochondrial membrane potential during cytokine deprivation. In this report, we demonstrate that activation of PPARγ also protects cells from serum starvation-induced apoptosis in human T lymphoma cell lines. Furthermore, we show that the survival effect of PPARγ is mediated through its actions on cellular metabolic activities. In cytokine-deprived cells, PPARγ attenuates the decline in ATP level and suppresses accumulation of reactive oxygen species (ROS). Moreover, PPARγ regulates ROS through its coordinated transcriptional control of proteins and enzymes involved in ROS scavenging, including uncoupling protein 2, catalase, and copper zinc superoxide dismutase. Our studies identify cell survival promotion as a novel activity of PPARγ and suggest that PPARγ may modulate cytokine withdrawal-induced activated T cell death.
Purpose: The Negative Symptom Assessment-16 (NSA-16) is an instrument with significant validity and utility for assessing negative symptoms associated with schizophrenia. This study aimed to validate the Chinese version of the NSA-16. Patients and Methods: A total of 172 participants with schizophrenia were assessed with the NSA-16, Scale for Assessment of Negative Symptoms (SANS), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS) and Rating Scale for Extrapyramidal Side Effects (RSESE). The factor structure of the NSA-16 was evaluated via exploratory and confirmatory factor analysis. Cronbach's α and intraclass correlation coefficients were computed. Correlations were evaluated via Spearman correlation coefficient. Results: The original five-factor model of the NSA-16 did not fit our sample. Exploratory factor analysis followed by confirmatory factor analysis suggested a three-factor structure, consisting of communication, emotion and motivation, with 15 items. The NSA with 15 items was termed as the NSA-15. The NSA-15 showed excellent convergent validity by high correlations with the SANS and PANSS total and negative factor scores and good divergent validity by independence from the PANSS positive factor, CDSS and RSESE. The NSA-15 showed good internal consistency, interrater reliability and test-retest reliability. Conclusion:The NSA-15 is best characterized by a three-factor structure and is valid for assessing negative symptoms of schizophrenia in Chinese individuals.
The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.
Selenium-containing small molecules have attracted considerable attention of chemical and medicinal researchers owing to their various biological activities, such as antitumor effects, cardiovascular protection, antibacterial or antiviral effects, immunoregulation and nerve protection, among which the most promising area is antineoplasm. In the past several decades, different kinds of organoselenium compounds, such as selenides, seleno(iso)cyanates, substituted selenoureas, selenious esters and Se-containing heterocycles have been reported as candidates of anti-cancer agents. Current reviews of Se-containing anticancer compounds mainly concerned about the investigation of their bioactivities, whereas, few attention has been addressed on their synthetic approaches. Herein, we summarized methodologies recently developed to synthesize organoselenium compounds with potent antineoplastic properties, which would be helpful for further design and synthesis of new bioactive Se-containing molecules with diverse structural features.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.