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Currently, Akebiae Caulis is being used in clinical practice, but there are few reseaches on its different varieties. To ensure the accuracy and effectiveness of clinical practice, this study distinguished the Akebia quinata (Thunb.) Decne. and Akebia trifoliata (Thunb.) Koidz, using organoleptic evaluation, microscopic observation, fluorescence reaction, physicochemical properties, thin-layer chromatography, IR spectroscopy, HPLC, four machine learning models, and in vitro antioxidant methods. Analysis of the powders of these two varieties using optical microscopy revealed the presence of starch granules, cork cells, crystal fibers, scalariform vessels, and wood fibers. Scanning electron microscopy revealed the presence of scalariform vessels, pitted vessels, wood fibers, and calcium oxalate crystals. Several tissues, including the cork layer, fiber population, cortex, phloem, pith, xylem, and ray, were found in the transverse section. In addition, thin-layer chromatography was used to identify two components: oleanolic acid and calceolarioside B; 11 common peaks were identified in 15 batches of SAQ and 5 batches of SAT by using HPLC. Support vector machine, BP neural networks, and GA-bp neural networks were able to predict 100% accurately of the different origins of stem of Akebia quinate (Thunb.) Decne (SAQ) and Akebia trifoliata (Thunb.) Koidz (SAT). Extreme learning machine achieved a correct rate of 87.5%. Meanwhile, Fourier-transform infrared spectroscopy fingerprint identified nine characteristic absorption peaks of the secondary metabolites of SAQ and SAT. 2,2-Diphenyl-1-1-picrylhydrazyl experiment revealed that the IC 50 values of SAQ and SAT extracts were 155.49 and 128.75 μg/ml, respectively. For the 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) assay, the IC 50 value of SAT extract was found to be 269.24 μg/ml, which was lower than that of SAQ extract (IC 50 = 358.99 μg/ml). This study successfully used different methods to differentiate between A. quinata (Thunb.) Decne. and A. trifoliata (Thunb.) Koidz., to help decide on which type to use for clinical application.

Titel:	Pharmacognostic standardization and machine learning-based investigations on Akebia quinata and Akebia trifoliata.
Autor/in / Beteiligte Person:	Qiu, J ; Shi, M ; Shi, S ; Wu, S
Link:	Zum Volltext
Zeitschrift:	Biomedical chromatography : BMC, Jg. 37 (2023-10-01), Heft 10, S. e5700
Veröffentlichung:	1990- : Chichester : Wiley ; <i>Original Publication</i>: London : Heyden & Son, c1986-1990, 2023
Medientyp:	academicJournal
ISSN:	1099-0801 (electronic)
DOI:	10.1002/bmc.5700
Schlagwort:	Chromatography, High Pressure Liquid Ranunculales Plant Extracts
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Biomed Chromatogr] 2023 Oct; Vol. 37 (10), pp. e5700. <i>Date of Electronic Publication: </i>2023 Jul 10. MeSH Terms: Ranunculales* ; Plant Extracts* ; Chromatography, High Pressure Liquid References: Boyanov, K. O., Choneva, M. A., Dimov, I. IV, Dimitrov, T. I. V., Gerginska, F. D., Delchev, S. D., Hrischev, P. I., Georgieva, K. N., & Bivolarska, A. V. (2022). Effect of oligosaccharides on the antioxidant, lipid and inflammatory profiles of rats with streptozotocin-induced diabetes mellitus. Zeitschrift fur Naturforschung C: Journal of Biosciences, 77, 379-386. https://doi.org/10.1515/znc-2021-0215. ; Câmara, A. B. F., de Oliveira, K. G., Santos, M. C. D., de Lima, R. R. S., de Lima, K. M. G., & de Carvalho, L. S. (2022). Multivariate assessment for predicting antioxidant activity from clove and pomegranate extracts by MCR-ALS and PLS models combined to IR spectroscopy. 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Koidz Substance Nomenclature: 0 (Plant Extracts) SCR Organism: Lardizabalaceae Entry Date(s): Date Created: 20230710 Date Completed: 20230918 Latest Revision: 20230918 Update Code: 20240514

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Pharmacognostic standardization and machine learning-based investigations on Akebia quinata and Akebia trifoliata.