Combination therapies for the treatment of oral squamous cell carcinoma have been studied extensively and represent a synergistic approach with better outcomes than monotherapy. In this study, a novel combination therapy was investigated using gold nanoparticles (GNP) conjugated to programmed cell death protein ligand 1 (PD-L1) antibodies and nonthermal plasma (NTP). The present study describes the effectiveness of NTP using PD-L1 antibody conjugated to GNP in PD-L1 expressing SCC-25 cells, an oral squamous cell carcinoma line. Immunocytochemistry revealed higher levels of PD-L1 expression and an increase in the selective uptake of PD-L1 antibody + GNP on SCC-25 cells compared to HaCaT cells. In addition, cell viability analyses confirmed higher levels of cell death of SCC-25 cells after treatment with PD-L1 antibody, GNP, and NTP compared to HaCaT cells. Among the experimental groups, the highest cell death was observed upon treatment with PD-L1 antibody + GNP + NTP. Following the Western blot analysis and immunofluorescence staining, the expression of apoptosis-related proteins was found to increase after treatment with PD-L1 antibody + GNP + NTP among the other experimental groups. In conclusion, the treatment of SCC-25 cells with PD-L1 antibody + GNP + NTP significantly increased the number of dead cells compared to other experimental groups. The results of this in vitro study confirmed the therapeutic effects of PD-L1 antibody + GNP + NTP treatment on oral squamous cell carcinoma.

The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.