Cellular toxicity evaluation of helical rosette nanotubes
Bibliographic record
Abstract
Helical rosette nanotubes (HRN) are synthesized through the molecular self‐assembly of individual rosette compounds. HRN are water‐soluble and synthesized in the absence of any metals. They have a range of potential applications including drug delivery. We have evaluated the potential in vitro toxicity of the HRN‐K1 compound in the Calu‐3 pulmonary epithelial cell line. Cells were treated with: Control (media only), Lysine (25μg/cm 2 ), 2.5, 12.5, 25μg/cm 2 of HRN‐K1 (HRN with lysine residue), 200μg/cm 2 and 60μg/cm 2 of quartz. Lysine and quartz were used as controls. Cells and supernatant samples were collected for analysis at 1, 12, and 24h post‐treatment. Cell viability was determined using Trypan Blue counting and a reduction in viability was detected in the high dose quartz group only. A gel electrophoresis assay showed DNA shearing in the high dose quartz group only. Preliminary ELISA data on cell supernatant indicate the release of IL‐8 in the quartz groups and 25μg/cm 2 HRN‐K1 group at 12 & 24h compared to media‐only control. Real time quantitative Rt‐PCR has also been performed for IL‐8, TNF‐α and ICAM‐1. In conclusion, HRN‐K1 does not reduce Calu‐3 cell viability or induce DNA strand breakage, but may activate cytokine release in vitro. These results contrast studies demonstrating cytotoxicity using single‐walled carbon nanotubes in vitro . (Support: NSERC NanoIP to B.S. and CIHR Doctoral Award to W.S.J.)
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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".