Distinguishing the effects of Ce nanoparticles from their dissolution products: identification of transcriptomic biomarkers that are specific for ionic Ce in<i>Chlamydomonas reinhardtii</i>
Bibliographic record
Abstract
Cerium (Ce) is a rare earth element that is incorporated in numerous consumer products, either in its cationic form or as engineered nanoparticles (ENPs). Given the propensity of small oxide particles to dissolve, it is unclear whether biological responses induced by ENPs will be due to the nanoparticles themselves or rather due to their dissolution. This study provides the foundation for the development of transcriptomic biomarkers that are specific for ionic Ce in the freshwater alga, Chlamydomonas reinhardtii, exposed either to ionic Ce or to two different types of small Ce ENPs (uncoated, ∼10 nm, or citrate-coated, ∼4 nm). Quantitative reverse transcription PCR was used to analyse mRNA levels of four ionic Ce-specific genes (Cre17g.737300, MMP6, GTR12, and HSP22E) that were previously identified by whole transcriptome analysis in addition to two oxidative stress biomarkers (APX1 and GPX5). Expression was characterized for exposures to 0.03-3 µM Ce, for 60-360 min and for pH 5.0-8.0. Near-linear concentration-response curves were obtained for the ionic Ce and as a function of exposure time. Some variability in the transcriptomic response was observed as a function of pH, which was attributed to the formation of metastable Ce species in solution. Oxidative stress biomarkers analysed at transcriptomic and cellular levels confirmed that different effects were induced for dissolved Ce in comparison to Ce ENPs. The measured expression levels confirmed that changes in Ce speciation and the dissolution of Ce ENPs greatly influence Ce bioavailability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".