Aluminium Nanoparticles Released by Joint Implants ‐ Quartz Crystal Microbalance with Dissipation Analysis
Bibliographic record
Abstract
Introduction The rising popularity of joint prosthetics such as total knee arthroplasty, total disc replacement or total hip arthroplasty has woken serious concern of toxicity of the nanoparticles (NPs) released by the prostheses because of corrosion and wear. The NPs are known to influence blood components, but their impact on platelets has not been yet analyzed. In this study we aimed to assess the effect of Al2O3 (Al) NPs on thrombocytes in vitro with the use of novel quartz crystal microbalance with dissipation (QCM‐D) methodology. Methods The several NPs (Al2O3 5nm, Al2O3 10nm, Al2O3 20nm, Al2O3 30nm, Al2O3 50nm and Al2O3 80nm) were tested for their ability to induce platelet activation and aggregation with use of flow cytometry, light transmission aggregometry and QCM‐D. The results of the latter test were confirmed with optical microscopy and transmission electron microscopy. Results Significant changes in dissipation (D) and frequency (F) were noted in platelet‐rich‐plasma incubated with all assessed NPs (concentration of 5, 2.5, 1, 0.5 μg/mL), in comparison to controls under flow. All tested NPs, except for the largest (80 nm) ones, induced a significant rise of both F and D, revealing thrombocyte aggregation even at the lowest tested concentration (0.5 μg/mL). Optical microscopy revealed platelet aggregation also for 80 nm NPs. In contrast, transmission electron microscopy of 80 nm NPs showed lack of platelet aggregation, however, the NPs caused formation of several platelets clumps similar to aggregates. Conclusion Our study provides evidence that Al NPs influence thrombocytes function in vitro. Moreover, different mechanism depending on the size of NPs has been noted. Smaller Al NPs induce platelet aggregation, whereas large particles penetrate the platelets, damaging them and releasing Ca2+ stores. It is not certain if the latter causes thrombosis in vivo. We believe that future in vivo studies might indicate safe aluminium level for patients with Al‐containing ceramic prostheses as well as establish which concentration would be optimal for revision surgery.
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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".