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Record W2789443412 · doi:10.1021/acs.jnatprod.7b00914

Analytical Method To Detect and Quantify Avocatin B in Hass Avocado Seed and Pulp Matter

2018· article· en· W2789443412 on OpenAlexafffund
Nawaz Ahmed, Richard W. Smith, Juan J. Aristizabal Henao, Ken D. Stark, Paul A. Spagnuolo

Bibliographic record

VenueJournal of Natural Products · 2018
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of WaterlooUniversity of Guelph
FundersHass Avocado BoardNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research ChairsUniversity of Guelph
KeywordsPulp (tooth)Coefficient of variationChemistryChromatographyDetection limitMedicine

Abstract

fetched live from OpenAlex

Avocatin B, an avocado-derived compound mixture, was demonstrated recently to possess potent anticancer activity by selectively targeting and eliminating leukemia stem cells. Avocatin B is a mixture of avocadene and avocadyne, two 17-carbon polyhydroxylated fatty alcohols (PFAs), first discovered in avocado seeds; their quantities in avocado pulp are unknown. Analytical methods to detect avocado seed PFAs have utilized NMR spectroscopy and GC-MS; both of these lack quantitative capacity and accuracy. Herein, we report a sensitive LC-MS method for the quantitation of avocadene and avocadyne in avocado seed and pulp. The method has a reliable and linear response range of 0.1–50 μM (0.03–17.2 ng/μL) for both avocadene and avocadyne (r2 > 0.990) with a lower limit of quantitation (LLOQ) of 0.1 μM. The intra- and interassay accuracy and precision of the quality control (QC) samples at LLOQ showed ≤18.2% percentage error and ≤14.4% coefficient of variation (CV). The intra- and interassay accuracy and precision for QC samples at low and high concentrations were well below 10% error and CV. This method was successfully applied to quantify avocadene and avocadyne in total lipid extracts of Hass avocado pulp and seed matter.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.327

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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

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