Expression of EMSY, a novel BRCA2-link protein, is associated with lymph node metastasis and increased tumor size in breast carcinomas

Background: The EMSY gene encodes a BRCA2-binding partner protein that represses the DNA repair function of BRCA2 in non-hereditary breast cancer. Although amplification of EMSY gene has been proposed to have prognostic value in breast cancer, no data have been available concerning EMSY tissue expression patterns and its associations with clinicopathological features. Materials and Methods: In the current study, we examined the expression and localization pattern of EMSY protein by immunohistochemistry and assessed its prognostic value in a well-characterized series of 116 unselected breast carcinomas with a mean follow up of 47 months using tissue microarray technique. Results: Immunohistochemical expression of EMSY protein was detected in 76 of primary breast tumors, localized in nuclear (18), cytoplasmic (35) or both cytoplasmic and nuclear sites (23). Univariate analysis revealed a significant positive association between EMSY expression and lymph node metastasis (p value=0.045) and larger tumor size (p value=0.027), as well as a non-significant relation with increased risk of recurrence (p value=0.088), whereas no association with patients' survival (log rank test, p value=0.482), tumor grade or type was observed. Conclusions: Herein, we demonstrated for the first time the immunostaining pattern of EMSY protein in breast tumors. Our data imply that EMSY protein may have impact on clinicipathological parameters and could be considered as a potential target for breast cancer treatment

Driver-Pedestrian Interactions at Unsignalized Crossings Are Not in Line With the Nash Equilibrium

Recent developments in vehicle automation require simulations of human-robot interactions in the road traffic context, which can be achieved by computational models of human behavior such as game theory. Game theory provides a good insight into road user behavior by considering agents' interdependencies. However, it is still unclear whether conventional game theory is suitable for modeling vehicle-pedestrian interactions at unsignalized locations or if more complex models like behavioral game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms, to answer this question. Unlike the most previous studies that employed naturalistic datasets to test and validate such models, this study utilized a distributed simulation dataset to test and compare the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator to replicate the traffic scenarios for 32 pedestrian-driver pairs. The findings demonstrated that there is a high variability between participant pairs' behaviors. Our proposed behavioral game-theoretic model outperformed other models in predicting the interaction outcome. This translates to a decrease by 70% and 67% in the root mean squared error (RMSE) when compared to the baseline model, for marked and unmarked crossings, respectively. The model can also predict which interaction will take the longest time to resolve. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory that underscores the complexity of human behavior with implications for the testing and development of automated vehicles

A Distributed Simulation Study to Examine Vehicle – Pedestrian Interactions

Current research on vehicle-pedestrian interactions focuses on the reaction of one actor other than the interaction of two actors, and considering the impact of the real-time behaviour of both actors on each other. To address this issue, the current study replicated a natural vehicle-pedestrian interaction to the virtual environment by connecting a high-fidelity driving simulator to a CAVE-based pedestrians' simulator. Behaviours from both actors in response to each other were observed indifferent situations including two crossing locations and five time gaps. The proposed method enabled simultaneous interaction in a controlled and safe environment as well as provided implications for future AV design

Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features

Observational molecular case-control study of genetic polymorphisms 1 in programmed cell death protein-1 in patients with oral lichen planus

Background: The association between programmed cell death protein 1 (PD-1) variations and susceptibility to autoimmune diseases has been recurrently reported. However, there is no report about its relationship with oral lichen planus (OLP) as one of autoimmune diseases. Methods: We investigated the association between genetic predisposition to OLP and two single nucleotide polymorphisms in PD-1. Results: GG, GA, and AA genotypes at position +7146 were found in 59 (80.8 ), 10 (13.7 ), and 4 (5.5 ) patients, and in 132 (77 ), 34 (20 ), and 5 (3 ) healthy participants. CC, CT, and TT genotypes at position +7785 were found in 32 (43.8 ), 35 (47.9 ), and 6 (8.2 ) patients and in 99 (58 ), 66 (39 ), and 6 (3 ) controls. Analysis indicated that patients' genotypes were not statistically different from controls' genotypes at both positions +7146 (P = 0.35 and P = 0.98) and +7785 (P = 0.07 and P = 0.06). Conclusion: The findings indicated that PD-1 SNPs at +7146 PD-1.3 G/A and +7785 PD-1.5 C/T was not associated with susceptibility to OLP. However, further research with higher sample size and in different geographical regions is needed in order to achieve the generalizability of the findings. © 2019, Asian Pacific Organization for Cancer Prevention

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95: 0.81�0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95: 0.81�0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. © 2022 The Author