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Record W3006118074 · doi:10.1039/c9fo02051a

A comparative study between Chinese propolis and Brazilian green propolis: metabolite profile and bioactivity

2020· article· en· W3006118074 on OpenAlexaff
Min Yuan, Xujiang Yuan, Miguel A. Pineda, Zeyu Liang, Jian He, Shengwei Sun, Tian-Ling Pan, Kunping Li

Bibliographic record

VenueFood & Function · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsInstitute of Infection and ImmunityCentre for Drug Research and Development
FundersScientific Research Foundation for Returned Scholars of Ministry of EducationNatural Science Foundation of Guangdong Province
KeywordsPropolisTraditional medicineAntioxidantMetaboliteChemistryBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Among different types, Chinese propolis (ChPs) and Brazilian green propolis (BrGPs) have been shown to contain multi-functional properties. Despite extensive research in the field, reports comparing propolis from different geographical areas are still limited, compromising our current understanding of the potential therapeutic effect associated with propolis and its derived compounds. Herein, a comparative study between ChPs and BrGPs including their metabolite profile and bioactivities was performed. Interestingly, even when ChPs and BrGPs showed similar anti-inflammatory potential, our results showed that they contained very different levels of ethanol extract, total flavonoids and total phenolic acids and in fact, LC-MS metabolic profiling and pattern recognition could effectively distinguish ChPs and BrGPs. Moreover, all the propolis samples tested showed good anti-oxidant activity and no significant difference of free radical scavenging capacity existed between ChPs and BrGPs. In conclusion, ChPs and BrGPs have a distinct chemome, but their antioxidant and anti-inflammatory activities are similar.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.036
GPT teacher head0.244
Teacher spread0.207 · 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 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

Citations43
Published2020
Admission routes1
Has abstractyes

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