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Lung cancer is one of the deadliest cancers worldwide due to the inability of existing methods for early diagnosis. Tumor-derived exosomes are nano-scale vesicles released from tumor cells to the extracellular environment, and their investigation can be very useful in both biomarkers for early cancer screening and treatment assessment. This research detected the exosomes via an ultrasensitive electrochemical biosensor containing gold nano-islands (Au-NIs) structures. This way, a high surface-area-to-volume ratio of nanostructures was embellished on the FTO electrodes to increase the chance of immobilizing the CD-151 antibody. In this way, a layer of gold was first deposited on the electrode by physical vapor deposition (PVD), followed by thermal annealing to construct primary gold seeds on the surface of the electrode. Then, gold seeds were grown by electrochemical deposition through gold salt. The cell-derived exosomes were successfully immobilized on the FTO electrode through the CD-151 antibody, and cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) methods were used in this research. In the CV method, the change in the current passing through the working electrode is measured so that the connection of exosomes causes the current to decrease. In the EIS method, surface resistance changes were investigated so that the binding of exosomes increased the surface resistance. Various concentrations of exosomes in both cell culture and blood serum samples were measured to test the sensitivity of the biosensor, which makes our biosensor capable of detecting 20 exosomes per milliliter. [ABSTRACT FROM AUTHOR]

Lung cancer is indeed a major cause of cancer‐related deaths worldwide. The development of tumors involves a complex interplay of genetic, epigenetic, and environmental factors. Epigenetic mechanisms, including DNA methylation (DNAm), histone modifications, and microRNA expression, play a crucial role in this process. Changes in DNAm patterns can lead to the silencing of important genes involved in cellular functions, contributing to the development and progression of lung cancer. MicroRNAs and exosomes have also emerged as reliable biomarkers for lung cancer. They can provide valuable information about early diagnosis and treatment assessment. In particular, abnormal hypermethylation of gene promoters and its effects on tumorigenesis, as well as its roles in the Wnt signaling pathway, have been extensively studied. Epigenetic drugs have shown promise in the treatment of lung cancer. These drugs target the aberrant epigenetic modifications that are involved in the development and progression of the disease. Several factors have been identified as drug targets in non‐small cell lung cancer. Recently, combination therapy has been discussed as a successful strategy for overcoming drug resistance. Overall, understanding the role of epigenetic mechanisms and their targeting through drugs is an important area of research in lung cancer treatment. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell's images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results. [ABSTRACT FROM AUTHOR]

Inadequacy of most animal models for drug efficacy assessments has led to the development of improved in vitro models capable of mimicking in vivo exposure scenarios. Among others, 3D multicellular spheroid technology is considered to be one of the promising alternatives in the pharmaceutical drug discovery process. In addition to its physiological relevance, this method fulfills high-throughput and low-cost requirements for preclinical cell-based assays. Despite the increasing applications of spheroid technology in pharmaceutical screening, its application, in nanotoxicity testing is still in its infancy due to the limited penetration and uptake rates into 3D-cell assemblies. To gain a better understanding of gold nanowires (AuNWs) interactions with 3D spheroids, a comparative study of 2D monolayer cultures and 3D multicellular spheroids was conducted using two lung cancer cell lines (A549 and PC9). Cell apoptosis (live/dead assay), metabolic activity, and spheroid integrity were evaluated following exposure to AuNWs at different dose-time manners. Results revealed a distinct different cellular response between 2D and 3D cell cultures during AuNWs treatment including metabolic rates, cell viability, dose–response curves and, uptake rates. Our data also highlighted further need for more physiologically relevant tissue models to investigate in depth nanomaterial–biology interactions. It is important to note that higher concentrations of AuNWs with lower exposure times and lower concentrations of AuNWs with higher exposure times of 3 days resulted in the loss of spheroid integrity by disrupting cell–cell contacts. These findings could help to increase the understanding of AuNWs-induced toxicity on tissue levels and also contribute to the establishment of new analytical approaches for toxicological and drug screening studies. [ABSTRACT FROM AUTHOR]