Effect of copper nanoparticles and copper ions on the architecture of rainbow trout olfactory mucosa
Bibliographic record
Abstract
Olfactory epithelial cells are in direct contact with myriad environmental contaminants which may consequently disrupt their structure and function. Copper ions (Cu2+) and copper nanoparticles (CuNPs) are two types of olfactory neurotoxicants. However, their effects on the structure of olfactory epithelium are largely uninvestigated. The density of olfactory goblet cells in CuNP- and Cu2+ - exposed rainbow trout was assessed using light microscopy throughout time. In both copper (Cu) treatments, the number of goblet cells increased initially over the 24 h exposure and then recovered to normal throughout the 96 h exposure. These data suggested the 96 h exposure to Cu contaminants interfered with protective barrier provided by goblet cells. Nonetheless, lamellar and epithelial thickness of olfactory rosette did not change in the Cu-exposed fish. The gene transcript profile of olfactory mucosa studied by RNA-seq indicated Cu2+ and CuNPs differentially targeted the molecular composition of cell junctions. In the Cu2+ treatment, reduced mRNA abundances of tight junctions, adherens junction, desmosomes and hemidesmosomes, suggest that Cu2+-exposed olfactory mucosal cells had weak junctional complexes. In the CuNP treatment, on the other hand, the transcript abundances of cell junction compositions, except adherens junction, were upregulated. Transcripts associated with gap junctional channels were increased in both Cu treatments. The elevated transcript levels of gap junctions in both Cu treatments suggested that the demand for intercellular communication was increased in the Cu-exposed olfactory mucosa. Overall, our findings suggested that Cu2+ induced greater adverse effects on the molecular composition of olfactory cell junctions relative to CuNPs. Impairment of junctional complexes may disrupt the structural integrity of olfactory mucosa.
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 | 0.000 | 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 | 0.001 |
| 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".