Effects of PVA coated nanoparticles on human immune cells
Bibliographic record
Abstract
Nanotechnology provides new opportunities in human medicine, mainly for diagnostic and therapeutic purposes. The autoimmune disease rheumatoid arthritis (RA) is often diagnosed after irreversible joint structural damage has occurred. There is an urgent need for a very early diagnosis of RA, which can be achieved by more sensitive imaging methods. Superparamagnetic iron oxide nanoparticles (SPION) are already used in medicine and therefore represent a promising tool for early diagnosis of RA. The focus of our work was to investigate any potentially negative effects resulting from the interactions of newly developed amino-functionalized amino-polyvinyl alcohol coated (a-PVA) SPION (a-PVA-SPION), that are used for imaging, with human immune cells. We analyzed the influence of a-PVA-SPION with regard to cell survival and cell activation in human whole blood in general, and in human monocytes and macrophages representative of professional phagocytes, using flow cytometry, multiplex suspension array, and transmission electron microscopy. We found no effect of a-PVA-SPION on the viability of human immune cells, but cytokine secretion was affected. We further demonstrated that the percentage of viable macrophages increased on exposure to a-PVA-SPION. This effect was even stronger when a-PVA-SPION were added very early in the differentiation process. Additionally, transmission electron microscopy analysis revealed that both monocytes and macrophages are able to endocytose a-PVA-SPION. Our findings demonstrate an interaction between human immune cells and a-PVA-SPION which needs to be taken into account when considering the use of a-PVA-SPION in human medicine.
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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".