A review on the techno‐functional, biological, and health‐promoting properties of hempseed‐derived proteins and peptides
Bibliographic record
Abstract
Protein-energy malnutrition is a global challenge that demands urgent attention, especially with the increasing population growth and unmatched food security plans. One strategy is to expand the list of protein sources, such as neglected and underutilized crops, with high protein content. A good number of plant proteins, in addition to their nutritional benefits, exert therapeutic properties as seen in seeds derived from legumes and emerging sources such as hemp. In this review, the transepithelial transport, functional, and biological properties of hempseed proteins (HSPs) and peptides were discussed. The review also described the potential safety issues of incorporating hempseeds in food products. Due to the multitargeted effects of hempseed-derived proteins and their peptides against many chronic diseases, and their functional properties, current knowledge shows that hempseed has tremendous potential for functional food and nutraceutical applications. PRACTICAL APPLICATIONS: The alarming rate of malnutrition and the attendant health consequences demand that underexploited nutrient-rich crops should be incorporated as part of our common dietary sources. Among these crops, hempseed is gaining attention as an emerging source of proteins and peptides with promising potential in prevention and management of chronic diseases such as diabetes, hypertension, cancer, hypercholesterolemia, obesity, and diseases whose etiology involves oxidative stress and inflammation. Fortunately, a growing body of research evidence is demonstrating that hempseed is a reservoir of proteins and peptides with nutraceutical potentials for curbing life-threatening diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".