Lung-Related Diseases Classification Using Deep Convolutional Neural Network

Accurate diagnosis is a crucial first step in the management and treatment of lung diseases, which include infectious diseases such as COVID-19, viral pneumonia, lung opacity, tuberculosis, and bacterial pneumonia. Despite these conditions sharing similar manifestations in chest X-ray images, it is imperative to correctly identify the disease present. This study, therefore, sought to develop a convolutional neural network (CNN)- based model for the classification of lung diseases. Four distinct CNN models, namely MobileNetV2, ResNet-50, ResNet-101, and AlexNet, were rigorously evaluated for their ability to classify lung diseases from chest X-ray images. These models were tested against three classification schemes to examine the impact of high interclass similarity: a 4-subclass classification (COVID-19, viral pneumonia, lung opacity, and normal), a 5-subclass classification (COVID-19, viral pneumonia, lung opacity, tuberculosis, and normal), and a 6-subclass classification (COVID-19, lung opacity, viral pneumonia, tuberculosis, bacterial pneumonia, and normal). The retrained ResNet-50 architecture yielded the best results, achieving a classification accuracy of 97.22%, 92.14%, and 96.08% for the 6-subclass, 5-subclass, and 4-subclass classifications respectively. Conversely, ResNet-101 demonstrated the lowest classification accuracy for the 6- subclass and 5-subclass classifications, with 78.12% and 79.49% respectively, while MobileNetV2 had the lowest accuracy for the 4-subclass classification, with 88.89%. These results suggest that, despite high interclass similarity, the ResNet-50 model can effectively classify lung-related diseases from chest X-ray images. This finding supports the use of computer-aided detection (CAD) systems as decision-support tools in the early classification of lung-related diseases

Chemotherapy-induced senescent cancer cells engulf other cells to enhance their survival.

In chemotherapy-treated breast cancer, wild-type p53 preferentially induces senescence over apoptosis, resulting in a persisting cell population constituting residual disease that drives relapse and poor patient survival via the senescence-associated secretory phenotype. Understanding the properties of tumor cells that allow survival after chemotherapy treatment is paramount. Using time-lapse and confocal microscopy to observe interactions of cells in treated tumors, we show here that chemotherapy-induced senescent cells frequently engulf both neighboring senescent or nonsenescent tumor cells at a remarkable frequency. Engulfed cells are processed through the lysosome and broken down, and cells that have engulfed others obtain a survival advantage. Gene expression analysis showed a marked up-regulation of conserved macrophage-like program of engulfment in chemotherapy-induced senescent cell lines and tumors. Our data suggest compelling explanations for how senescent cells persist in dormancy, how they manage the metabolically expensive process of cytokine production that drives relapse in those tumors that respond the worst, and a function for their expanded lysosomal compartment