Phenolic bioactives from developmental stages of highbush blueberry (<i>Vaccinium corymbosum</i>) for hyperglycemia management using in vitro models
Bibliographic record
Abstract
Cheplick, S., Sarkar, D., Bhowmik, P. and Shetty, K. 2015. Phenolic bioactives from developmental stages of highbush blueberry (Vaccinium corymbosum) for hyperglycemia management using in vitro models. Can. J. Plant Sci. 95: 653–662. Blueberry is a rich source of soluble phenolics as well as human health relevant antioxidants. Phenolic-linked bioactive functionality of blueberry for type 2 diabetes management was screened during fruit maturation, especially from green to ripening stages using in vitro assays. Green fruit showed the highest total soluble phenolic content, whereas all three developmental stages of blueberry fruit exhibited high total antioxidant activity. Overall, ripe fruit had higher α-amylase and α-glucosidase inhibitory activity than green or green/pink fruit, and showed significant potential to improve glucose metabolism through in vitro assays. High phenolic-linked antioxidant activity along with moderate to high α-amylase and α-glucosidase inhibitory activity in ripe blueberry indicated its potential relevance as part of diet-based prevention and management of early stages of hyperglycemia associated with development of type 2 diabetes. This in vitro screening study provides a biochemical rationale and dietary strategy to develop the right blueberry cultivar and stage of fruit development for further validation in animal and clinical studies.
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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 | 0.001 |
| 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".