<p>Effects of functionalized silver nanoparticles on aggregation of human blood platelets</p>
Bibliographic record
Abstract
Purpose: We studied the effects of silver nanoparticles (AgNPs) on human blood platelet function. We hypothesized that AgNPs, a known antimicrobial agent, can be used as blood-compatible, “ideal material’’ in medical devices or as a drug delivery system. Therefore, the aim of the current study was to investigate if functionalized AgNPs affect platelet function and platelets as well as endothelial cell viability in vitro. Methods: AgNPs, functionalized with reduced glutathione (GSH), polyethylene glycol (PEG) and lipoic acid (LA) were synthesized. Quartz crystal microbalance with dissipation was used to measure the effect of AgNPs on platelet aggregation. Platelet aggregation was measured by changes in frequency and dissipation, and the presence of platelets on the sensor surface was confirmed and imaged by phase contrast microscopy. Flow cytometry was used to detect surface abundance of platelet receptors. Lactate dehydrogenase test was used to assess the potential cytotoxicity of AgNPs on human blood platelets, endothelial cells, and fibroblasts. Commercially available ELISA tests were used to measure the levels of thromboxane B 2 and metalloproteinases (MMP-1, MMP-2) released by platelets as markers of platelet activation. Results: 2 nm AgNPs-GSH, 3.7 nm AgNPs-PEG both at 50 and 100 μg/mL, and 2.5 nm AgNPs-LA at 100 μg/mL reduced platelet aggregation, inhibited collagen-mediated increase in total P-selectin and GPIIb/IIIa, TXB2 formation, MMP-1, and MMP-2 release. The tested AgNPs concentrations were not cytotoxic as they did not affect, platelet, endothelial cell, or fibroblast viability. Conclusion: All tested functionalized AgNPs inhibited platelet aggregation at nontoxic concentrations. Therefore, functionalized AgNPs can be used as an antiplatelet agent or in design and manufacturing of blood-facing medical devices, such as vascular grafts, stents, heart valves, and catheters. Keywords: functionalized silver nanoparticles, blood platelets, QCM-D, platelets receptors, TXB 2 , MMP-1 and MMP-2
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".