MétaCan
Menu
Back to cohort
Record W2318785714 · doi:10.1021/np5001413

In Vitro Evaluation of Potential Bitterness-Masking Terpenoids from the Canada Goldenrod (<i>Solidago canadensis</i>)

2014· article· en· W2318785714 on OpenAlexaboutno aff
Jie Li, Pan Li, Joshua N. Fletcher, Wei Lv, Ye Deng, Michael A. Vincent, Jay P. Slack, T. Scott McCluskey, Zhonghua Jia, Mark Cushman, A. Douglas Kinghorn

Bibliographic record

VenueJournal of Natural Products · 2014
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLabdaneTerpenoidDiterpeneOleanolic acidChemistryChloroformTerpeneStereochemistryTraditional medicineBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

In a screening of extracts of selected plants native to Ohio against the human bitterness receptor hTAS2R31, a chloroform-soluble extract of the aerial parts of Solidago canadensis (Canada goldenrod) was determined to have hTAS2R31 antagonistic activity and, thus, was fractionated for isolation of potential bitterness-masking agents. One new labdane diterpenoid, solidagol (1), and six known terpenoids, including two labdane diterpenoids (2 and 3), three clerodane diterpenoids (6β-angeloyloxykolavenic acid, 6β-tigloyloxykolavenic acid, and crotonic acid), and a triterpenoid (longispinogenin), were isolated. Among these compounds, 3β-acetoxycopalic acid (2) was found to be the first member of the labdane diterpene class shown to have inhibitory activity against hTAS2R31 activation (IC50 8 μM). A homology model of hTAS2R31 was constructed, and the molecular docking of 2 to this model indicated that this diterpenoid binds well to the active site of hTAS2R31, whereas this was not the case for the closely structurally related compound 3 (sempervirenic acid). The content of 2 in the chloroform-soluble portion of the methanolic extract of S. canadensis was up to 2.24 g/100 g dry weight, as determined by HPLC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.170
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.246
Teacher spread0.236 · 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 teacher head, 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".

Quick stats

Citations27
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Natural ProductsSame topicBiochemical Analysis and Sensing TechniquesFrench-language works237,207