Cobalt ions sensitize TRPV2 and TRPV4 channels in synovial fibroblasts, leading to cytokine secretion
Bibliographic record
Abstract
Cobalt-chromium alloys are widely used in orthopedic implants due to their excellent toughness, wear resistance, and biocompatibility. However, cobalt ions released as consequence of corrosion or wear, trigger cytokine secretion and promote inflammation and pain in periprosthetic tissues. Transient receptor potential (TRP) channels are a family of voltage-dependent Ca 2+ permeable channels involved in various physiological and pathological processes. Because of their permeability and modulation by divalent cations, we studied how TRP channels’ activity is influenced by cobalt ions. We used primary human synovial fibroblasts and through qPCR we found relevant expression of TRPC1, TRPC4, TRPV2, TRPV4, TRPM4 and TRPM7 mRNA in synovial fibroblasts. Next, we exposed synovial fibroblasts to cobalt ions and/or selective pharmacology of TRPV2 and TRPV4 channels. We observed that TRPV2 and TRPV4 are sensitized by cobalt exposure, increasing intracellular calcium in synovial fibroblasts. Furthermore, exposure to TRPV2 and TRPV4 antagonists inhibited the basal long-term intracellular calcium increase, and reduced the secretion of IL-6, IL-8, TNF-a, and VEGF-a triggered by cobalt exposure. However, the sole activation of TRPV2 and TRPV4 did not trigger secretion or expression of these cytokines. Our findings demonstrate for the first time that metal ions released from orthopedic implants, can modulate the function of TRP channels and may contribute to the pathogenesis of fibrosis and inflammation associated with biomedical implants. Notably, we propose a molecular mechanism in which TRPV2 and TRPV4 channels are potentially involved in mediating inflammatory and fibrotic responses in peri-implant tissues. However, further studies are necessary to elucidate the regulatory role of cytosolic calcium in the development of adverse local tissue reactions.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".