THE INFLUENCE OF pH ON ALGAL CELL MEMBRANE PERMEABILITY AND ITS IMPLICATIONS FOR THE UPTAKE OF LIPOPHILIC METAL COMPLEXES<sup>1</sup>
Bibliographic record
Abstract
Uptake of lipophilic metal complexes by freshwater algae has recently been shown to be pH dependent. Here we look at different physiological aspects that could influence the diffusion of the lipophilic Cd complex, Cd(diethyldithiocarbamate)2 (0) (Cd(DDC)2 (0) ), into algal cells at different exposure pH values. Changes in cell membrane permeability were assessed as a function of pH for three species of green algae [Chlamydomonas reinhardtii P. A. Dang., Pseudokirchneriella subcapitata (Korshikov) Hindák, and Chlorella fusca var. vacuolata Shihira et R. W. Kraus] using two neutral, nonionic probes, fluorescein diacetate (FDA) and D-sorbitol. In parallel experiments, we exposed algae to inorganic Cd or to Cd(DDC)2 (0) and monitored Cd intracellular metal distribution, together with phytochelatin synthesis. For the three algal species acclimated at pH 5.5 (w/wo DDC 1 μM) and exposed at this pH, their permeability to FDA and D-sorbitol was consistently lower than for algae growing at pH 7.0 and exposed at this pH (P < 0.001). The ratio of the FDA hydrolysis rate measured at pH 7.0 with respect to the rate measured at pH 5.5 (both in the presence of DDC) correlated with the ratio of the Cd(DDC)2 (0) initial internalization rate constant obtained at pH 7.0 versus that obtained at pH 5.5 (three algae species, n = 9, r = 0.85, P = 0.004). Our results strongly suggest that acidification affects metal availability to algae not only by proton inhibition of facilitated metal uptake but also by affecting membrane permeability.
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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".