Lethal and Sublethal Toxicity of Nanosilver and Carbon Nanotube Composites to Hydra vulgaris—A Toxicogenomic Approach
Bibliographic record
Abstract
The increasing use of nanocomposites has raised concerns about the potential environmental impacts, which are less understood than those observed with individual nanomaterials. The purpose of this study was to investigate the toxicity of nanosilver carbon-walled nanotube (AgNP–CWNT) composites in Hydra vulgaris. The lethal and sublethal toxicity was determined based on the characteristic morphological changes (retraction/loss of tentacles and body disintegration) for this organism. In addition, a gene expression array was optimized for gene expression analysis for oxidative stress (superoxide dismutase, catalase), regeneration and growth (serum response factor), protein synthesis, oxidized DNA repair, neural activity (dopamine decarboxylase), and the proteasome/autophagy pathways. The hydras were exposed for 96 h to increasing concentrations of single AgNPs, CWNTs, and to 10% AgNPs–90% CWNTs, and 50% AgNPs–50% CNWT composites. Transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDS) analysis revealed the presence of AgNPs attached to the carbon nanotubes and AgNP aggregates. The data revealed that the AgNP–CWNT composites were more toxic than their counterparts (AgNPs and CNWT). The sublethal morphological changes (EC50) were strongly associated with oxidative stress and protein synthesis while lethal morphological changes (LC50) encompassed changes in dopamine activity, regeneration, and proteasome/autophagic pathways. In conclusion, the toxicity of AgNP–CWNT composites presents a different pattern in gene expression, and at lower threshold concentrations than those obtained for AgNPs or CWNTs alone.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".