Differential cytotoxic and inflammatory potency of amorphous silicon dioxide nanoparticles of similar size in multiple cell lines
Bibliographic record
Abstract
The likelihood of environmental and health impacts of silicon dioxide nanoparticles (SiNPs) has risen, due to their increased use in products and applications. The biological potency of a set of similarly-sized amorphous SiNPs was investigated in a variety of cells to examine the influence of physico-chemical and biological factors on their toxicity. Cellular LDH and ATP, BrdU incorporation, resazurin reduction and cytokine release were measured in human epithelial A549, human THP-1 and mouse J774A.1 macrophage cells exposed for 24 h to suspensions of 5-15, 10-20 and 12 nm SiNPs and reference particles. The SiNPs were characterized in dry state and in suspension to determine their physico-chemical properties. The dose-response data were simplified into particle potency estimates to facilitate the comparison of multiple endpoints of biological effects in cells. Mouse macrophages were the most sensitive to SiNP exposures. Cytotoxicity of the individual cell lines was correlated while the cytokine responses differed, supported by cell type-specific differences in inflammation-associated pathways. SiNP (12 nm), the most cytotoxic and inflammogenic nanoparticle had the highest surface acidity, dry-state agglomerate size, the lowest trace metal and organics content, the smallest surface area and agglomerate size in suspension. Particle surface acidity appeared to be the most significant determinant of the overall biological activity of this set of nanoparticles. Combined with the nanoparticle characterization, integration of the biological potency estimates enabled a comprehensive determination of the cellular reactivity of the SiNPs. The approach shows promise as a useful tool for first-tier screening of SiNP toxicity.
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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".