Effect of Acetaminophen and Nonsteroidal Anti-Inflammatory Drugs on Gene Expression of Mesenchymal Stem Cells
Bibliographic record
Abstract
We have previously shown that mesenchymal stem cells (MSCs) from patients with osteoarthritis (OA) constitutively express type X collagen, a marker of late-stage chondrocyte hypertrophy, osteogenic marker genes, including alkaline phosphatase (ALP), bone sialoprotein (BSP), and osteocalcin (OC), and chondrogenesis marker gene aggrecan (ACAN). As patients with arthritis often take nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen (Acet), the purpose of the study was to assess whether these drugs can affect the gene expression of human MSCs. MSCs isolated from the bone marrow of patients with OA or normal donors were cultured without (control) or with Acet or NSAIDs, which include ibuprofen, diclofenac (Dic), naproxen, and celebrex. After 3 days of culture, the expression of type X collagen alpha 1 (COL10A1), ACAN, COL1A1, as well as ALP, BSP, OC, and Runt-related transcription factor 2 was analyzed by real-time reverse transcription (RT)-polymerase chain reaction. The results showed that COL10A1 and the osteogenic and chondrogenic marker genes can be regulated by NSAIDs and Acet in normal MSCs. In contrast, Acet did not significantly affect COL10A1 expression in OA MSCs, while Dic is the only drug that had no significant effect on all markers in normal MSCs. The upregulation of COL10A1 in normal MCSs by Acet and Npx may explain why stem cells from patients with OA express COL10A1 constitutively. This knowledge may help in designing better strategies for stem cell differentiation into chondrocyte-like cells, from this source, with Dic being a viable option for treating OA pain, with an eye toward preventing the potential to enhance calcification in the repair of cartilage and degenerated intervertebral discs.
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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".