Lee HJ, et al. J Periodontal Implant Sci. 2015 Jun;45(3):120-126. https://doi.org/10.5051/jpis.2015.45.3.120

The reduction of tumor cell proliferation and antimutaginicity of the essential oil from Agastache rugosa O. Kuntze and its major components such as methyl chavicol (82.8%), limonene (3.9%) and anisaldehyde (2.6%) were investigated by using four human carcinoma cell lines. Both the oil and its three major components were shown to possess low cytotoxicity on normal human liver cells, WRL68 by only inhibiting less than 12% of normal cell growth. Its inhibition percentage was much less than those from other medicinal herb extracts. The oil had relatively strong antimutaginicity when screened against a genetically engineered Chinese Hamster Ovary (CHO AS-52) cell line, and the results of antimutaginicity were shown to be closely related to the effect of inhibiting cancer cell growth. In general, the oil had better inhibition effect on cancer cells than individual components: 63% for crude oil vs. 49% for anisaldehyde at 1 g/L of the treatment. It was found that the oil could effectively inhibit the growth of human lung cancer cell up to 82 %, having 0.09 of IC₅₀. The proliferation of other human cancer cell lines have also been retarded in the range of 40—80% by addition of the oil. This was well matched with the result of antimutaginicity as follows: 75% for the oil and 38–63% for individual oil components at 1 g/L, respectively. The functions of antimutaginicity and inhibiting cancer cell growth of the components were also confirmed by the kinetic analysis of human immune cell growth. The growth of both B and T cell lines was enhanced up to about 200% by adding the oil, but the oil components could not improve the human T cells. The results of a kinetic analysis using a microphysiometer revealed that the oil could affect human B cell growth rapidly within 10 min of the treatment showing up to 180% enhancement of the growth. While the individual components only reacted after 20 min. This may be interpreted that the oil (mixture of components) can have a synergistic effect on the growth of human T cells and better inhibition of cancer cell growth. These results also imply that the oil could selectively inhibit the proliferation of human cancer cells (>1.5 of the selectivity) better than the individual oil components, possibly due to the synergistic effect of the three major components and/or the combined effects by other unknown minor components in the oil.

This study quantified projection-dependent geometric distortion induced by vertical camera angulation in two-dimensional (2D) dental image acquisition and evaluated its implications for integration with three-dimensional (3D) CAD/CAM and artificial intelligence (AI)-driven workflows. To our knowledge, this study is among the first to use quantitative methods to characterize projection-induced distortion across the dental arch as a function of vertical camera angulation. Fourteen fully dentate casts were photographed at nine standardized vertical angulations using a controlled acquisition setup based on the standardized occlusal plane angle (SOPA). Tooth surface areas were measured through digital tracing and analyzed with a mixed-effects model (α = 0.05). Significant associations were identified between vertical camera angulation and measured tooth surface area for all teeth except canines (p < 0.05 for all except canines). Anterior teeth demonstrated increased apparent surface area at superior camera angulations, whereas posterior teeth were more prominently represented at inferior angulations. Central incisors, lateral incisors, and first premolars exhibited maximal visibility above the occlusal plane, while second premolars and molars were more optimally visualized below it. These findings indicate that vertical camera angulation induces non-uniform, region-specific geometric distortion across the dental arch. From a computational perspective, these distortions represent a systematic source of variability in 2D photographic datasets used in CAD/CAM workflows, virtual smile design, and AI-assisted image analysis. Because modern machine learning systems depend on geometrically consistent input data, uncorrected projection-induced distortion may reduce the reliability and generalizability of downstream algorithmic outputs. Accordingly, the present findings establish a quantitative basis for recognizing projection-induced variability in 2D dental photographs and support future development of geometry-aware calibration strategies for 2D-3D digital integration. AI-assisted correction represents a future translational direction contingent upon explicit alignment between acquisition geometry, image formation, and computational modeling.