Energy cost of intracellular metal and metalloid detoxification in wild-type eukaryotic phytoplankton
Bibliographic record
Abstract
Microalgae use various cellular mechanisms to detoxify both non-essential and excess essential metals or metalloids. There exists however, a threshold in intracellular metal(loid) concentrations beyond which detoxification mechanisms are no longer effective and inhibition of cell division inevitably occurs. It is therefore important to determine whether the availability of energy in the cell could constrain metal(loid) detoxification capacity and to better define the thresholds beyond which a metal(loid) becomes toxic. To do this we performed the first extensive bioenergetics analysis of intracellular metal(loid) detoxification mechanisms (e.g., metal-binding peptides, polyphosphate granules, metal efflux, metal and metalloid reduction, metalloid methylation, enzymatic and non-enzymatic antioxidants) in wild-type eukaryotic phytoplankton based on the biochemical mechanisms of each detoxification strategy and on experimental measurements of detoxifying biomolecules in the literature. The results show that at the onset of metal(loid) toxicity to growth, all the detoxification strategies considered required only a small fraction of the total cellular energy available for growth indicating that intracellular detoxification ability in wild-type eukaryotic phytoplankton species is not constrained by the availability of cellular energy. The present study brings new insights into metal(loid) toxicity mechanisms and detoxification strategies in wild-type eukaryotic phytoplankton.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".