Recent research directions on functional royal jelly: highlights prospects in food, nutraceutical, and pharmacological industries
Bibliographic record
Abstract
The food and pharmaceutical industries have utilized royal jelly, an alternative medicinal food, as a natural pharmaceutical product since ancient times. Royal jelly has a unique remarkable composition containing lipids, proteins, carbohydrates, vitamins, minerals, hormones, and phenolic compounds. The rapidly expanding functional food market has coincided with the increasing consumer demand for royal jelly. Over the past two decades, royal jelly, a rich source of certain bioactive components, has been used by humans as a functional and nutritious food due to recent studies of the effect of royal jelly in underlying pathogenic processes in a variety of animal models. Scientific evidence has accumulated supporting a wide variety of health-promoting effects from the intake of royal jelly that supports cardiovascular health, immune and antioxidant function, wound healing, blood lipid, and glucose control in addition to antibacterial and antihypertensive effects. The main bioactive ingredients are Major Royal Jelly Proteins (MRJPs), essential oils, fatty acids, peptides, and phenolics, which are thought to have a significant role in the development of honeybee queens. The health-endorsing qualities of royal jelly make it a significant functional ingredient in the food, and cosmetic industry. Apisin is one of the main proteins in royal jelly that has antibacterial properties. Other bioactive ingredients of royal jelly that have multifunctional health-promoting properties include defensin-1, royalisin, apisimin, apidaecin, jelleins, royalactin and 10-hydroxy-2-decenoic acid (10HDA) in epigenetic diseases. This review highlights the important role that royal jelly plays as an agent in various fields of medicine, paying special attention to its biological features. Additionally, we discuss royal jelly's composition as a possible therapeutic for vital natural sources of bioactive substances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".