Aryl Hydrocarbon Receptor Activation Ameliorates Acute Respiratory Distress Syndrome through Regulation of Th17 and Th22 Cells in the Lungs.

Acute respiratory distress syndrome (ARDS) is triggered by a variety of insults, including bacterial and viral infections, and this leads to high mortality. While the role of the aryl hydrocarbon receptor (AhR) in mucosal immunity is being increasingly recognized, its function during ARDS is unclear. In the current study, we investigated the role of AhR in LPS-induced ARDS. AhR ligand, indole-3-carbinol (I3C), attenuated ARDS which was associated with a decrease in CD4+ RORγt +IL-17a+IL-22+ pathogenic Th17 cells, but not CD4+RORγt +IL-17a+IL-22- homeostatic Th 17 cells, in the lungs. AhR activation also led to a significant increase in CD4+IL-17a-IL-22+ Th22 cells. I3C-mediated Th22 cell expansion was dependent on the AhR expression on RORγt+ cells. AhR activation downregulated miR-29b-2-5p in immune cells from the lungs, which in turn downregulated RORc expression and upregulated IL-22. Collectively, the current study suggests that AhR activation can attenuate ARDS and may serve as a therapeutic modality by which to treat this complex disorder. IMPORTANCE Acute respiratory distress syndrome (ARDS) is a type of respiratory failure that is triggered by a variety of bacterial and viral infections, including the coronavirus SARS-CoV2. ARDS is associated with a hyperimmune response in the lungs that which is challenging to treat. Because of this difficulty, approximately 40% of patients with ARDS die. Thus, it is critical to understand the nature of the immune response that is functional in the lungs during ARDS as well as approaches by which to attenuate it. AhR is a transcription factor that is activated by a variety of endogenous and exogenous environmental chemicals as well as bacterial metabolites. While AhR has been shown to regulate inflammation, its role in ARDS is unclear. In the current study, we provide evidence that AhR activation can attenuate LPS-mediated ARDS through the activation of Th22 cells in the lungs, which are regulated through miR-29b-2-5p. Thus, AhR can be targeted to attenuate ARDS.

LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.