Bibliographic record
Abstract
Tea (Camellia sinensis), as a global drink and functional food, has attracted more attention to its anti-inflammatory activities over the past few years.The vast prevalence of inflammatory diseases and calls for prevention and control have motivated scientists to explore the nature's anti-inflammatory bioactive compounds in tea and their mechanisms.This study describes the major anti-inflammatory compounds of tea, including tea polyphenols, theaflavins and their derivatives, theanine, caffeine, and polysaccharides and volatile oils, and their molecular mechanisms in regulating oxidative stress, inhibiting inflammatory signaling pathways, modulating cytokine expression, influencing immune cell function, and changing gut microbiota.It is also furthered to provide the most recent advances from animal models, human intervention trials, and in vitro studies, and discusses the promise of tea's anti-inflammatory activity in functional foods, medicinal foods, and dermatological preparations.In the face of challenges like complexity of active ingredients, processing effects, insufficiency of sufficient clinical evidence, and safety of dosage, this review also discusses multi-dimensional research integration and comparative evaluation of different study outcomes, establishing scientific basis for public health and industry pragmatic significance of tea's anti-inflammatory research.This study offers theoretical justification for the production of tea-based natural anti-inflammatory products and directs future experimental and clinical investigation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".