Processing of Metals and Metalloids by Actinobacteria: Cell Resistance Mechanisms and Synthesis of Metal(loid)-Based Nanostructures
Bibliographic record
Abstract
Metal(loid)s have a dual biological role as micronutrients and stress agents. A few geochemical and natural processes can cause their release in the environment, although most metal-contaminated sites derive from anthropogenic activities. Actinobacteria include high GC bacteria that inhabit a wide range of terrestrial and aquatic ecological niches, where they play essential roles in recycling or transforming organic and inorganic substances. The metal(loid) tolerance and/or resistance of several members of this phylum rely on mechanisms such as biosorption and extracellular sequestration by siderophores and extracellular polymeric substances (EPS), bioaccumulation, biotransformation, and metal efflux processes, which overall contribute to maintaining metal homeostasis. Considering the bioprocessing potential of metal(loid)s by Actinobacteria, the development of bioremediation strategies to reclaim metal-contaminated environments has gained scientific and economic interests. Moreover, the ability of Actinobacteria to produce nanoscale materials with intriguing physical-chemical and biological properties emphasizes the technological value of these biotic approaches. Given these premises, this review summarizes the strategies used by Actinobacteria to cope with metal(loid) toxicity and their undoubted role in bioremediation and bionanotechnology fields.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".