Phenol-Rich Feijoa sellowiana (Pineapple Guava) Extracts Protect Human Red Blood Cells from Mercury-Induced Cellular Toxicity
Bibliographic record
Abstract
Plant polyphenols, with broadly known antioxidant properties, represent very effective agents against environmental oxidative stressors, including mercury. This heavy metal irreversibly binds thiol groups, sequestering endogenous antioxidants, such as glutathione. Increased incidence of food-derived mercury is cause for concern, given the many severe downstream effects, ranging from kidney to cardiovascular diseases. Therefore, the possible beneficial properties of Feijoa sellowiana against mercury toxicity were tested using intact human red blood cells (RBC) incubated in the presence of HgCl2. Here, we show that phenol-rich (10–200 µg/mL) extracts from the Feijoa sellowiana fruit potently protect against mercury-induced toxicity and oxidative stress. Peel and pulp extracts are both able to counteract the oxidative stress and thiol decrease induced in RBC by mercury treatment. Nonetheless, the peel extract had a greater protective effect compared to the pulp, although to a different extent for the different markers analyzed, which is at least partially due to the greater proportion and diversity of polyphenols in the peel. Furthermore, Fejioa sellowiana extracts also prevent mercury-induced morphological changes, which are known to enhance the pro-coagulant activity of these cells. These novel findings provide biochemical bases for the pharmacological use of Fejioa sellowiana-based functional foods in preventing and combating mercury-related illnesses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".