Cobalt Nanoparticles Cause Major Platelet Aggregation In Vitro While Chromium Nanoparticles Induce Platelet Lysis In Vitro.
Bibliographic record
Abstract
Introduction The usage of artificial joint prostheses has recently gained significant popularity, with roughly two million total hip, knee and spinal arthroplasties performed annually worldwide. However, the presence of prostheses in organism is associated with significant release of nanoparticles (NPs). When in the bloodstream they can affect various blood components, such as platelets. Despite that the problem of NPs’ influence on platelets function has not been thoroughly analyzed. Therefore the aim of our study was to analyze the impact of Cr and Co particles on thrombocytes in vitro with usage of novel quartz crystal microbalance with dissipation (QCM‐D) methodology. Materials and methods The light transmission aggregometry, flow cytometry and QCM‐D were utilized to assess the ability of NPs to cause activation and aggregation of platelets. Transmission electron microscopy (TEM), scanning electron microscopy and optical and immunofluorescence microscopy were used to confirm the outcomes given by QCM‐D. Results Compared to the controls under flow there was a significant change of frequency and dissipation in platelet‐rich plasma incubated with Co 28nm, CoO 50nm, Co2O3 50nm, Co3O4 30–50nm, Cr 35–45nm, Cr2O3 60nm NPs (at concentrations of 5, 2.5, 1, 0.5 μg/mL). The NPs induced platelet aggregation even at the lowest concentrations. Other utilized modalities confirmed aggregation of thrombocytes. Furthermore, TEM showed that Cr NPs induce swelling and lysis. Conclusion Our study indicates that both Cr and Co NPs influence thrombocytes function in vitro. Also two different underlying mechanisms of platelet aggregation as Cr NPs cause swelling and lysis while Co particles induce typical aggregation. We conclude that any physician should take into consideration monitoring level of Cr and Co NPs of patients having Co‐Cr prosthesis.
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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".