Chemical Analysis and Effect of Blueberry and Lingonberry Fruits and Leaves against Glutamate-Mediated Excitotoxicity
Bibliographic record
Abstract
Phenolic compounds are a large class of phytochemicals that are widespread in the plant kingdom and known to have antioxidant capacities. This study aimed to determine the antioxidant capacities as well as the content of total soluble phenolics, anthocyanins, tannins, and flavonoids in the fruits and leaves of blueberries and lingonberries growing in Newfoundland. This study also determined the potential neuroprotective effect of extracts from fruits and leaves against glutamate-mediated excitotoxicity, which is believed to contribute to disorders such as stroke and neurodegenerative diseases. Lingonberry and blueberry plants were found to be rich sources of phenolic compounds. Total antioxidant capacities in terms of radical scavenging activity and reducing power were much higher in leaves of both plants as compared to their fruits. These results were in correlation with phenolic contents including total flavonoids, anthocyanins, and tannins. Brain-derived cell cultures from rats were prepared and grown for about 2 weeks. Cell cultures were treated with glutamate (100 μM) for 24 h, and the effect of extracts was determined on cells subjected to this excitotoxicity. Glutamate treatment caused approximately 23% cell loss when measured after 24 h of exposure. Whereas lingonberry fruit extract did not provide protection from glutamate toxicity, blueberry fruit extracts were extremely protective. Leaf extracts of both lingonberry and blueberry showed a significant neuroprotective effect. The greater protective effect of leaf extracts was in correlation with the levels of phenolics and antioxidant capacity. These findings suggest that berries or their components may contribute to protecting the brain from various pathologies.
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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".