In Vitro Effects of Arthrocen, an Avocado/Soy Unsaponifiables Agent, on Inflammation and Global Gene Expression in Human Monocytes
Bibliographic record
Abstract
Osteoarthritis (OA) is the most common form of arthritis. Symptomatically characterized by stiffness and pain, OA is a chronic degenerative disease of joints. Of note, there is growing interest in the potential use of plant-based compounds for symptomatic treatment of OA. Arthrocen is a plant-derived agent consisting of a one to two ratio of avocado and soy unsaponifiable extracts. In order to decipher the potential mechanisms of Arthrocen's action at the molecular level, we employed an in vitro assay using cultured human THP-1 cells (a model cell line for monocytes) to study its effects. By pairing protein arrays enriched for inflammatory markers, transcriptomic pathway analysis using RNA-Sequencing, and eicosanoid specific lipidomics, we have begun to unravel its potential mechanism of action. Specifically, we found that Arthrocen can attenuate the inflammatory response at the transcript level while inducing significant changes in numerous cytokines. Furthermore, we discovered that while Arthrocen alone did not increase IL-8 or MCP-1 levels, its presence had a synergistic effect on the observed increase in response to LPS stimulation. Additionally, this synergistic effect of Arthrocen on LPS stimulation of IL-8 and MCP-1 protein levels was also observed at the mRNA level and suggests a regulatory mechanism at the transcriptional level. Interestingly, Arthrocen induced no changes in any of the eicosanoids studied. This multi-omics approach implies that Arthrocen functions at the level of gene transcription to dampen inflammation mediated by monocytes in OA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".