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Record W2052259665 · doi:10.1002/rcm.2650

Mass spectral characterization of phloroglucinol derivatives hyperforin and adhyperforin

2006· article· en· W2052259665 on OpenAlexafffund
Lekha Sleno, Rambod Daneshfar, Gunter P. Eckert, Dietrich A. Volmer

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsInstitute for Marine BiosciencesDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryHyperforinPhloroglucinolFourier transform ion cyclotron resonanceInfrared multiphoton dissociationMass spectrometryTandem mass spectrometryFragmentation (computing)Collision-induced dissociationDissociation (chemistry)ProtonationIon cyclotron resonanceIonHypericum perforatumAnalytical Chemistry (journal)ChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Active phloroglucinol constituents of Hypericum perforatum (St. John's wort) extracts, hyperforin and adhyperforin, have been studied following ion activation using tandem mass spectrometry (MS/MS) and complemented by accurate mass measurements. These two compounds were readily analyzed as protonated and deprotonated molecules with electrospray ionization. MS/MS and MS3 data from a quadrupole-linear ion trap tandem mass spectrometer were employed to elucidate fragmentation pathways. Fourier transform ion cyclotron resonance measurements afforded excellent mass accuracies for the confirmation of elemental formulae of product ions formed via infrared multiphoton dissociation and sustained off-resonance irradiation collision-induced dissociation. Fragmentation schemes have been devised for the dissociation of hyperforin and adhyperforin in negative and positive ion modes. This information is expected to be especially valuable for the characterization of related compounds, such as degradation products, metabolites and novel synthetic analogs of hyperforin.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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 designObservational
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

Citations13
Published2006
Admission routes2
Has abstractyes

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