<i>Curcuma longa</i> and Its Bioactive Curcuminoids: Molecular Mechanisms in Anti-inflammatory and Immunomodulation
Bibliographic record
Abstract
Curcuma longa and its major bioactive compound, curcumin, have been used widely in traditional medicine and have attracted wide research attention worldwide for their prominent anti-inflammation and immunomodulatory effects in recent years.This study summarizes the chemical properties of C. longa and curcumin, their major bioactive constituents, and the mechanism of their synergistic actions, focusing on inhibiting inflammatory responses through the modulation of the NF-κB, MAPK, JAK/STAT, and PI3K/Akt/mTOR signaling pathways to regulate innate and adaptive immunity, inflammasomes, and the activity of immune-related cells.It integrates the progress in the in vitro, animal, and clinical research, discussing bioavailability, metabolism, and gut microbiota interactions on their physiological activities.Safety, dosage, possible risks, and challenges in translation into pharmaceutical applications are analyzed.Being natural products, C. longa and curcumin possess huge potential in the prevention and treatment of chronic inflammation-related diseases.More studies in mechanistic elucidation and clinical validation would be required to promote the clinical application of C. longa and curcumin.In addition, this study has helped gain further insight into the molecular mechanisms of the therapeutic properties of C. longa and curcumin, which provides the scientific basis necessary for developing and applying C. longa and curcumin as natural anti-inflammatory and immunomodulatory agents in the management of chronic diseases.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".