Anti-inflammatory effects of polymethoxyflavones from citrus peels: a review
Bibliographic record
Abstract
Inflammation is a non-specific kind of biological immune response of body tissues to any type of external or internal injuries, such as pathogens, irritants and immune stress reactions. There are two types of inflammation, namely acute and chronic. Acute inflammation starts and develops rapidly, and is aroused by various factors, including injuries, infection, toxins or immune reactions. Chronic inflammation usually lasts for an extended long period of time and results from elimination failure of acute inflammation, autoimmune disorders, various pathogens and pathogenic environments. Except for the damage itself, there exists a direct and intimate connection between chronic inflammation and various clinic common diseases, such as neurodegeneration, as well as metabolic and cardiovascular ailments. Citrus peel is a by-product generated in citrus juice processing. Polymethoxyflavones (PMFs) exist abundantly and almost exclusively in citrus peels, and their biological activities have been broadly investigated in recent years. PMFs have proven to possess potential inhibitory bioactivities towards a number of functional and immune diseases including inflammation. The two most abundant PMFs exhibiting prominent bioactivities in citrus peels are nobiletin and tangeretin, ubiquitously detected in various citrus species. In this review, the beneficial health effects and the underlying molecular mechanisms of ten main citrus PMFs were illustrated against numerous inflammatory diseases, including inflammatory bowel disease (IBD), neuroinflammation and organ inflammation, among others.
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 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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".