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Record W1983670770 · doi:10.1055/s-2009-1216481

Chemical Fingerprint Analysis of Two Centella Species, Quantification of Triterpenoids and its Glycosides by using HPTLC Method

2009· article· en· W1983670770 on OpenAlexfundno aff
CS Rumalla, Bharathi Avula, YH Wang, AD Weerasooriya, TJ Smillie, IA Khan

Bibliographic record

VenuePlanta Medica · 2009
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plants and Neuroprotection
Canadian institutionsnot available
FundersNational Institute on Drug AbuseAgricultural Research ServiceUniversity of British ColumbiaUniversity of Illinois at Urbana-ChampaignChinese Academy of SciencesKurukshetra UniversityUniversity of Illinois at ChicagoNational Oceanic and Atmospheric AdministrationU.S. Food and Drug AdministrationNational Science Foundation of Sri LankaUniversity of ColomboU.S. Department of AgricultureWestern Carolina UniversityInternational Science CouncilNational Center for Complementary and Alternative MedicineChina Academy of Traditional Chinese MedicineNational Institutes of HealthNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesTürkiye Bilimsel ve Teknolojik Araştırma KurumuHong Kong Polytechnic UniversityNational Science FoundationDeutsche KrebshilfeNational Natural Science Foundation of ChinaTata TrustsUniversity Grants CommissionWake Forest University
KeywordsCentellaChromatographyGlycosideChemistrySilica gelHigh performance thin layer chromatographyTriterpenoidTraditional medicineFingerprint (computing)Thin-layer chromatographyStereochemistryMedicine

Abstract

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Centella asiatica (L.) Urb. (Family Apiaceae commonly known as Gotu Kola or Indian Pennywort) has long been used in the Ayurvedic system of medicine for improving memory and for the treatment of a variety of ailments [1]. The triterpenoid compounds purportedly represent the chief pharmacologically active constituents. The triterpenoids, especially asiticoside, triterpine trisaccharide, are reported as the most active compounds in the plant [2]. A simple and fast method was developed for the quantitative determination of four triterpenes and their glycosides i.e. asiatic acid (AA), madecassic acid (MA), asiaticoside (AS) and madecoside (MS) in Centella asiatica and Centella erecta by using high performance thin layer chromatographic method. The separation was achieved with chloroform: methanol: water: 13.0:6.5:0.5 v/v/v on silica gel 60 F 254 HPTLC plates. Quantitation was performed with densitometry in absorption-reflection mode at 600 nm by scanning the HPTLC plates after a color development by anisaldehyde reagent. The linear regression data for the calibration plots showed a good linear relationship with r = 0.999, 0.998, 0.997 and 0.998 for asiatic acid, madecassic acid, asiaticoside and madecoside, respectively. The established method was validated in terms of LOD and LOQ, linearity. Acknowledgements: This research is funded in part by The United States Department of Agriculture Specific Cooperative Research Agreement Number 58-6408-6-067 and the FDA/CFSAN grant entitled Science Based Authentication of Dietary Supplements Number 2 U01 FD 002071-07 References: [1] Shakir JS, et al. (2007), Nat. Prod. Radiance, 6 (2): 158–170. [2] de Paula Reis, et al. (1996), Revista Brasileira de Farmácia, 77(2): 71–72. Fig. 1 (A) Centella asiatica , (B) Standard mix, (C) Centella erecta . Table 1 Validation Parameters. Parameter AA MA AS MS 1 Linearity range (ng/spot) 200–600 200–600 100–500 100–500 2 Correlation coefficient 0.999 0.998 0.997 0.998 4 LOD (ng/spot) 30 60 30 30 5 LOQ (ng/spot) 180 200 100 100 6 Specificity Specific Specific Specific Specific 7 Regression equation Y =94.580 +8.961 X Y =61.937 +3.124 X Y =22.600 +0.495 X Y =12.773 +0.113 X 8 Rf 0.72 0.61 0.17 0.11 Table 2 Percentage (w/w) of asiatic acid, madecassic acid, asiticoside, and madecoside in plant sample. Sample name (Percentage in dry plant material) AA MA AS MS C. asiatica 0.2 0.2 3.6 2.0 C.erecta 0.1 0.1 4.5 3.1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.342
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2009
Admission routes1
Has abstractyes

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