Effects of preharvest factors on antidiabetic potential of some foods and herbal plants
Bibliographic record
Abstract
Diabetes is a metabolic disorder with no definite treatment, but it can be controlled by changing lifestyle and diet. Consumption of high-fiber and nutrient-rich foods including vegetables have been shown to reduce risks of obesity and Type II Diabetes Mellitus (T2DM). Also, many herbal plants have been associated with reduced risks of T2DM because of their composition of secondary metabolites. Antioxidant activities of some secondary metabolites have potent inhibitory effects against inflammation linked with insulin resistance and oxidative stress. More than 800 known medicinal plants are used to control diabetes and its relevant complications. However, variations in preharvest factors including plant genotype, growing medium properties, climatic factors, and management practices can influence plant growth and their accumulation of phytochemicals with health-promoting properties. However, the effects of these preharvest factors on the antidiabetic properties of plant secondary metabolites are neither explicit nor easily accessible in the literature. Therefore, this review aims to document recent studies that reported on under-exploited medicinal plants with antidiabetic properties. We reviewed several important preharvest factors that can potentially affect the synthesis of phytoconstituents which possess antidiabetic properties. This review will help identify gaps for future research in phytomedicine and functional foods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".