Study of Anti-Inflammatory Activity of Fatsiphloginum™ (Fatsia japonica) and a New Purified Triterpene-Rich Extract of Saponins (PS-551) in Experimental Model of Arthritis

In this study, two extracts from Fatsia japonica-Fatsiphloginum™ (extract of triterpene glycosides containing 45-50 % of fatsiosides (FS)) and purified triterpene-rich extract of saponins with code name PS-551 (PS) were administered in combination with methotrexate (MTX) and in monotherapy to rats suffering adjuvant arthritis (AA). The anti-inflammatory activities of extracts were evaluated as monotherapies in comparison with untreated AA. PS administered in higher dose showed on day 28 effective decrease of hind paw volume (HPV), decreased activity of gamma-glutamyl transferase (GGT) in joints, and also interleukin-17A was decreased significantly on day 14. The higher dose of PS was more effective than both doses of FS. Further, we evaluated the higher doses of PS and FS in combination with MTX. PS improved the effect of MTX in combination more effective than FS (HPV, body weight and activity of GGT in joint). However, FS was more effective in reducing the level of IL-17A on day 14 and activity of GGT in spleen than PS. In conclusion, our study showed that generally FS has higher anti-arthritic activity comparing to PS. Thus, the novel combination of Fatsiphloginum™ and methotrexate could be interesting for future clinical studies in patients suffering auto-immune diseases.

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.