Curcumin increases gelatinase activity in human neutrophils by a p38 mitogen-activated protein kinase (MAPK)-independent mechanism
Bibliographic record
Abstract
Curcumin has been found to possess anti-inflammatory activities and neutrophils, key players in inflammation, were previously found to be important targets to curcumin in a few studies. For example, curcumin was found to induce apoptosis in neutrophils by a p38 mitogen-activated protein kinase (MAPK)-dependent mechanism. However, the role of curcumin on the biology of neutrophils is still poorly defined. To study the role of curcumin on neutrophil degranulation and to determine the role of p38 MAPK, human neutrophils were freshly isolated from healthy individuals and incubated in vitro with curcumin. Degranulation was studied at three levels: surface expression of granule markers by flow cytometry; release of matrix metallopeptidase-9 (MMP-9 or gelatinase B) enzyme into supernatants by Western blot; and gelatinase B activity by zymography. Activation of p38 MAPK was studied by monitoring its tyrosine phosphorylation levels by western blot and its role by the utilization of a pharmacological inhibitor. The results indicate that curcumin increased the cell surface expression of CD35 (secretory vesicle), CD63 (azurophilic granules), and CD66b (gelatinase granules) in neutrophils. Also, curcumin increased the release and enzymatic activity of gelatinase B in the extracellular milieu and activated p38 MAP kinase in these cells. However, in contrast to fMLP, curcumin-induced enzymatic activity and secretion of gelatinase B were not reversed by use of a p38 inhibitor. Finally, it was found that curcumin was able to enhance phagocytosis. Taken together, the results here demonstrate that curcumin induced degranulation in human neutrophils and that the increased gelatinase activity is not dependent on p38 MAPK activation. Therefore, degranulation is another human neutrophil function that could be modulated by curcumin, as well as phagocytosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".