Macrophage Inflammatory Response to Self‐Assembling Rosette Nanotubes
Bibliographic record
Abstract
Rosette nanotubes (RNTs) are a new class of nanomaterials with significant therapeutic potential. However, societal concerns related to the potential adverse health effects of engineered nanomaterials drew attention towards the investigation of their interaction with the human U937 macrophage cell line. The cells are treated with medium only (control), lysine (50 microg mL(-1)), lysine-functionalized RNTs (RNT-K; 1, 5, and 50 microg mL(-1)), Min-U-Sil quartz microparticles (80 microg mL(-1)), or lipopolysaccharide (1 microg mL(-1)). The supernatant and cells are assayed for cell viability, cytokine protein, and mRNA expression at 1, 6, and 24 h post-treatment. The results indicate that RNT-K activate transcription of proinflammatory genes (interleukin-8 and tumor necrosis factor-alpha (TNF-alpha)) within 1 h, but this effect is not accompanied by protein secretion into the supernatant. The effect of the length of RNTs on human U937 macrophage viability is also investigated. Although both short and long RNT-K exhibit time-dependent effects on TNF-alpha transcription, only the short RNT-K (5 microg mL(-1)) increase TNF-alpha concentration at 6 h relative to the long RNT-K. Moreover, RNT-K (1 and 5 microg mL(-1)) have no effect on cell viability by 24 h. These data indicate that RNT-K do not induce a robust inflammatory response or cytotoxicity in the U937 human macrophage cell line, and therefore could be used for biomedical applications.
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 | 0.000 | 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".