Cadmium accumulation and toxicity in the unicellular alga <i>Pseudokirchneriella subcapitata</i>: Influence of metal-binding exudates and exposure time
Bibliographic record
Abstract
Predicting metal availability and toxicity for chronic (several hours or days) metal exposure scenarios, even for unicellular algae, is a major challenge to existing toxicity models. This is because several factors affecting metal uptake and toxicity, such as the release of metal-binding exudates, changes in the kinetics of metal uptake and toxicity over time, and algal physiological acclimation to internalized metals, are still poorly understood. The present study assessed the influence of these factors on Cd uptake and toxicity in laboratory batch cultures of the freshwater alga Pseudokirchneriella subcapitata. To do so, changes in the free Cd(2+) concentrations caused by the release of metal-binding algal exudates were monitored, (109)Cd accumulation in algal cells was measured, and Cd-induced inhibition of algal growth as a function of exposure time (from 12 h to 96 h) was followed. Results indicate that metal-binding exudates may decrease the proportion of the free Cd(2+) ion in solution up to 2-fold, a decrease that affects Cd uptake and toxicity. Pseudokirchneriella subcapitata has the capacity to decrease net Cd uptake rate on short time scales (<24 h), but this reduction in the Cd uptake rate disappeared after 24 h, and Cd toxicity occurred at relatively high Cd concentrations in solution. These data illustrate some of the pitfalls of standard algal toxicity assays, which were designed for acute exposures, and suggest how robust chronic bioassays might be developed.
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 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) | 0.000 | 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".