Pro-inflammatory activity of contaminating DNA in hyaluronic acid preparations
Bibliographic record
Abstract
Hyaluronic acid (HA), an abundant non-sulfated glycosaminoglycan component of the extracellular matrix, has applications in drug delivery, tissue engineering and as an ingredient in cosmetics. HA preparations containing high-molecular-weight polymers are also used in the treatment of inflammatory disorders such as arthritis and interstitial cystitis. Low-molecular-weight fragments derived from HA have been reported to induce pro-inflammatory cytokines such as IL-12 and TNF-alpha, and could therefore potentially exacerbate existing inflammation. We therefore examined the pro-inflammatory activity of HA preparations, since inflammatory reactions are known to occur following administration of HA. We tested low-molecular-weight fragments obtained from seven different HA preparations, either by sonication (approximately equals 3 x 10(5) Da) or by hyaluronidase digestion (approximately equals 1 x 10(4) Da), for the ability to induce the synthesis of IL-12 and TNF-alpha by human monocytic cells. We found that two of the seven HA preparations tested stimulated the synthesis of IL-12 and TNF-alpha by human monocytic cells. We unexpectedly found that the induction of IL-12 and TNF-alpha by these HA preparations was not due to their degradation to low-molecular-weight fragments, since their native high-molecular-weight forms possessed the same ability to stimulate IL-12 and TNF-alpha synthesis, but was due to the presence of contaminating DNA. Treatment of these two HA preparations with deoxyribonuclease I abrogated or reduced the induction of IL-12 and TNF-alpha. It is clear from this study that HA preparations can induce the synthesis of pro-inflammatory cytokines by monocytes. The ability of HA to act as a pro-inflammatory mediator may not, however, be related to the presence of low-molecular-weight HA fragments, but to the presence of DNA. The presence of pro-inflammatory DNA in HA preparations should be evaluated before its use, not only for the treatment of patients with inflammatory disorders, but also before many other applications.
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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 | 0.001 | 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".