Compositional Differences Between Brazilian and Chinese Propolis
Bibliographic record
Abstract
Abstract: Background: There has been an increased prevalence of positive patch test reactions to propolis in recent years. Different reaction rates have been described when using propolis supplied by different manufacturers. Objective: Compare compositions of Brazilian propolis prepared by Allergeaze and Chinese propolis prepared by Chemotechnique. Methods: Both samples were analyzed using electrospray ionization mass spectrometry, and compounds were identified via the mzCloud, ChemSpider, and MassList databases. Data processing with Compound Discoverer software identified the top 6 compounds based on relative abundance. Results: A very low compositional overlap between the 2 propolis types: 8% match with ChemSpider, 9% with MassList, and 27% with mzCloud. The six most abundant compounds in Brazilian propolis included lauryldimethylamine oxide, (9Z)-9-octadecenamide, trioctylmethylammonium cation, monocillin VI and istamycin C1, while Chinese propolis contained pinocembrin, 16-([ethylcarbamoyl]amino) hexadecanoic acid, (4E)-6-hydroxy-4-octadecenoic acid/bee glue, linoleamide, and MFCD00083068. Prenylgermacrene B was the only common compound in both samples’ top 6. Conclusion: These findings highlight significant compositional differences between Brazilian and Chinese propolis. Chinese propolis (catalog number NA71) was discontinued by Allergeaze in October 2019 and replaced by Brazilian propolis (catalog number NH400), likely contributing to the increased prevalence of positive reactions in recent years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".