Potential cellular targets of platinum in the freshwater microalgae Chlamydomomas reinhardtii and Nitzschia palea revealed by transcriptomics
Bibliographic record
Abstract
Abstract Platinum group element (PGE) levels have increased in natural aquatic environments in the last few decades, in particular as a consequence of the use of automobile catalytic converters on a global scale. This raises questions regarding their possible impacts on aquatic ecosystems, as the natural background concentrations of these rare metals are extremely low. Primary producers, such as microalgae, are of great ecological importance, as they are at the base of the food web. The purpose of this work was to better understand the impact of Pt on a cellular level for freshwater unicellular algae. Two species with different characteristics, a green alga C. reinhardtii and a diatom N. palea , were studied. The bioaccumulation of Pt as well as its effect on growth were quantified. The use of molecular tools also allowed for the determination of possible intracellular effects and pathways of Pt. Both species that could be experiencing copper deficiency: inductions of genes corresponding to copper transporter suggest that Pt might be internalized through the Cu(I) metabolic pathway. Moreover, Pt could possibly be excreted using an efflux pump. Other highlights include a concentration dependant negative impact of Pt on mitochondrial metabolism for C. reinhardtii which is not observed for N. palea .
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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".