Effect of sonication and serum proteins on copper release from copper nanoparticles and the toxicity towards lung epithelial cells
Bibliographic record
Abstract
Pontus Cronholma, Klara Midanderb, Hanna L. Karlssona, Karine Elihnc, Inger Odnevall Wallinderb & Lennart Möller*aa Unit for Analytical Toxicology, Department of Biosciences and Nutrition at Novum, Karolinska Institutet, Stockholm, Swedenb Division of Surface and Corrosion Science, Department of Chemistry, School of Chemical Science and Engineering, Royal Institute of Technology, Stockholmc Workplace Aerosols Research Group, Department of Applied Environmental Science, Stockholm University, Stockholm, SwedenCorrespondence: Prof. Lennart Möller, Dr Med Sci, Karolinska Institutet, Biosciences and Nutrition, Novum, Huddinge, Huddinge, SE-141 57 Sweden. lennart.moller@ki.se AbstractDifferent methodological settings can influence particle characteristics and toxicity in nanotoxicology. The aim of this study was to investigate how serum proteins and sonication of Cu nanoparticle suspensions influence the properties of the nanoparticles and toxicological responses on human lung epithelial cells. This was investigated by using methods for particle characterization (photon correlation spectroscopy and TEM) and Cu release (atomic absorption spectroscopy) in combination with assays for analyzing cell toxicity (MTT-, trypan blue- and Comet assay). The results showed that sonication of Cu nanoparticles caused decreased cell viability and increased Cu release compared to non-sonicated particles. Furthermore, serum in the cell medium resulted in less particle agglomeration and increased Cu release compared with medium without serum, but no clear difference in toxicity was detected. Few cells showed intracellular Cu nanoparticles due to fast release/dissolution processes of Cu. In conclusion; sonication can affect the toxicity of nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".