Search

Purpose: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals. Methods: We retrieved anonymized imaging and clinical data from an institutional database, focusing on patients who underwent curative pulmonary resections for non-small cell lung cancers. Our study used a deep learning model, the Mask Region-based Convolutional Neural Network (MRCNN), to predict cancer locations and assign recurrence classification scores. To find optimized trained weighted values in the model, we developed preprocessing python codes, adjusted dynamic learning rate, and modifying hyper parameter in the model. Results: The model training completed; we performed classifications using the validation dataset. The results, including the confusion matrix, demonstrated performance metrics: bounding box (0.390), classification (0.034), mask (0.266), Region Proposal Network (RPN) bounding box (0.341), and RPN classification (0.054). The model successfully identified lung cancer recurrence sites, which were then accurately mapped onto chest CT images to highlight areas of primary concern. Conclusion: The trained model allows clinicians to focus on lung regions where cancer recurrence is more likely, acting as a significant aid in the detection and diagnosis of lung cancer. Serving as a clinical decision support system, it offers substantial support in managing lung cancer patients. [ABSTRACT FROM AUTHOR]

Tumor progression and metastasis are regulated by endothelial cells undergoing endothelial–mesenchymal transition (EndoMT), a cellular differentiation process in which endothelial cells lose their properties and differentiate into mesenchymal cells. The cells undergoing EndoMT differentiate through a spectrum of intermediate phases, suggesting that some cells remain in a partial EndoMT state and exhibit an endothelial/mesenchymal phenotype. However, detailed analysis of partial EndoMT has been hampered by the lack of specific markers. Transforming growth factor‐β (TGF‐β) plays a central role in the induction of EndoMT. Here, we showed that inhibition of TGF‐β signaling suppressed EndoMT in a human oral cancer cell xenograft mouse model. By using genetic labeling of endothelial cell lineage, we also established a novel EndoMT reporter cell system, the EndoMT reporter endothelial cells (EMRECs), which allow visualization of sequential changes during TGF‐β‐induced EndoMT. Using EMRECs, we characterized the gene profiles of multiple EndoMT stages and identified CD40 as a novel partial EndoMT‐specific marker. CD40 expression was upregulated in the cells undergoing partial EndoMT, but decreased in the full EndoMT cells. Furthermore, single‐cell RNA sequencing analysis of human tumors revealed that CD40 expression was enriched in the population of cells expressing both endothelial and mesenchymal cell markers. Moreover, decreased expression of CD40 in EMRECs enhanced TGF‐β‐induced EndoMT, suggesting that CD40 expressed during partial EndoMT inhibits transition to full EndoMT. The present findings provide a better understanding of the mechanisms underlying TGF‐β‐induced EndoMT and will facilitate the development of novel therapeutic strategies targeting EndoMT‐driven cancer progression and metastasis. [ABSTRACT FROM AUTHOR]

The Nitrogen Oxides (NOx) from engines aggravate natural environment and human health. Institutional regulations have attempted to protect the human body from them, while car manufacturers have tried to make NOx free vehicles. The formation of NOx emissions is highly dependent on the engine operating conditions and being able to predict NOx emissions would significantly help in enabling their reduction. This study investigates advanced method of predicting vehicle NOx emissions in pursuit of the sensorless engine. Sensors inside the engine are required to measure the operating condition. However, they can be removed or reduced if the sensing object such as the engine NOx emissions can be accurately predicted with a virtual model. This would result in cost reductions and overcome the sensor durability problem. To achieve such a goal, researchers have studied numerical analysis for the relationship between emissions and engine operating conditions. Also, a Deep Neural Network (DNN) is applied recently as a solution. However, the prediction accuracies were often not satisfactory where hyperparameter optimization was either overlooked or conducted manually. Therefore, this study proposes a virtual NOx sensor model based on the hyperparameter optimization. A Genetic Algorithm (GA) was adopted to establish a global optimum with DNN. Epoch size and learning rate are employed as the design variables, and R-squared based user defined function is adopted as the object function of GA. As a result, a more accurate and reliable virtual NOx sensor with the possibility of a sensorless engine could be developed and verified.