Multidimensional assessment of nutritional composition, contaminants and biological properties of bee pollen
Bibliographic record
Abstract
Honey-bee collected pollen is renowned for its nutritional richness and potential health benefits, but compositional variations and contamination are significant challenges. This research investigates the organic and inorganic content, morphological aspects, and anticancer properties of 18 different pollen samples from Canada (Québec). Results revealed variability in macronutrients (carbohydrates: 39.1 to 57.0 g 100g −1 , proteins: 14.3 to 23.5 g 100g −1 , and lipids: 3.58 to 35.4 g 100g −1 ) and micronutrients, including vitamin B and C (up to 1052 µg g −1 ) and antioxidants. However, pollen was found to contain pesticide residues (diazinon, thiamethoxam and glyphosate at up to 32.1 ng g −1 ) and heavy metals (lead and arsenic at up to 3.86 mg kg −1 ), indicating the need for environmental monitoring, including regular assessment of pollen contamination and implementation of mitigation strategies to reduce exposure. Cytotoxicity assays showed promising anticancer effects against HeLa cells, with up to 40% cell growth inhibition with an IC 50 at 247 µg mL −1 was observed in cells treated with pollen extract compared to untreated cells. Future research should focus on profiling bioactive compounds and their bioavailability while establishing standardized characterization methods and a centralized database for accurate nutritional and safety assessments. This study provides novel insights into the composition of pollen, its biological effects and contamination levels, illustrating its nutritional and therapeutic potential.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".