Comprehensive Review of Bioactive Compounds in Loquat and Their Pharmacological Mechanisms
Bibliographic record
Abstract
Loquat (Eriobotrya japonica Lindl.) is a subtropical fruit tree with significant medicinal value that has been widely used in traditional Chinese medicine throughout history.Various parts of the loquat plant, including its leaves and fruits, contain numerous bioactive compounds that exhibit a wide range of pharmacological activities.This study comprehensively summarizes the bioactive compounds found in loquat and elucidates their pharmacological mechanisms.Research indicates that the main bioactive compounds in loquat include phenolics, terpenes, kaempferol, ursolic acid, oleanolic acid, and quercetin, which possess strong antioxidant, anti-inflammatory, antidiabetic, antitumor, and antibacterial properties.The bioactive compounds in loquat can improve conditions such as diabetes, non-alcoholic fatty liver disease (NAFLD), and hyperlipidemia by inhibiting cytochrome P450 2E1, reducing oxidative stress, and regulating metabolic pathways.Additionally, studies have found that loquat leaves and fruits have high antioxidant capacities, which are closely related to their phenolic content.These findings highlight the potential of loquat as a source of bioactive compounds with significant health benefits.Further research into the bioavailability, metabolism, and toxicity of these compounds is crucial for fully realizing their therapeutic potential.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".