PCT-233, a novel modulator of pro- and anti-inflammatory cytokine production
Bibliographic record
Abstract
Plant extracts have been implicated in various immunoregulatory effects that are poorly understood. Thus, we investigated the modulatory activity of PureCell Complex (PCT)-233, an active molecular complex from mesophyll tissue of Spinacia oleacea on the inflammatory process. Alveolar macrophages (AM) were treated with PCT-233 and/or budesonide, a well-known anti-inflammatory agent, before or after being stimulated with lipopolysaccharides (LPS). Pro- and anti-inflammatory cytokine production, tumour necrosis factor (TNF) and interleukin (IL)-10, respectively, were measured in cell-free supernatants at different times after the treatment. PCT-233 increased unstimulated AM release of both TNF and IL-10, whereas heat- and light-inactivated PCT-233 stimulated only the release of TNF without affecting IL-10 production, suggesting that different mechanisms are involved in the modulation of TNF and IL-10 release by PCT-233. The presence of LPS did not modify PCT-233-stimulated TNF production, but the ratio TNF/IL-10 production by LPS-stimulated AM was reduced significantly in the presence of PCT-233. Pretreatment of AM with PCT-233 and budesonide before LPS stimulation reduced TNF production at both protein and mRNA levels, whereas IL-10 production was increased. Moreover, TNF/IL-10 ratio was reduced further with the combination PCT-233/budesonide. Interestingly, AM treatment with PCT-233 and budesonide 18 h after LPS stimulation did not modulate TNF release significantly but it did increase IL-10 production, and a synergistic effect was observed with the combination PCT-233/budesonide. These exciting data suggest that PCT-233 possesses some anti-inflammatory properties, even when added during the inflammatory process, and could potentiate the effect of other anti-inflammatory agents.
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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) | 0.001 | 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".