Intracellular distribution of cadmium during the growth of <i>Abortiporus biennis</i> on cadmium-amended media
Bibliographic record
Abstract
Heavy metals are difficult to remediate and traditional remedial processes are expensive, so bioremediation technology using bacteria, fungi, or plants is of interest. Many studies have demonstrated that basidiomycetes fungi are able to growth under heavy metals stress. In this study the distribution of cadmium (Cd) in Abortiporus biennis cells was studied. Cd accumulated especially within cytoplasm and its presence caused changes in the cytoplasm appearance, which became denser in comparison to the cytoplasm of control cells. Vacuolization of cytoplasm and periplasmic region in A. biennis cells was also observed. The growth rate of A. biennis was inhibited up to 75% during the growth on medium amended with 1 mmol/L cadmium oxide. The presence of Cd in growing media inhibited oxalic acid secretion by A. biennis, but oxalate concentration increased together with elevated Cd concentration in growing medium. The influence of initial pH of growing media on the accumulation of Cd by A. biennis was also observed. The highest accumulation of Cd in mycelium was detected during A. biennis growth on media with a pH of 6. Studies addressing metals uptake by fungi and metal distribution in fungal cells may allow these organisms to be applied in bioremediation processes more effectively or to be used as bioindicators of contaminated environmental pollutions.
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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".