Tellurite and Selenite: how can these two oxyanions be chemically different yet so similar in the way they are transformed to their metal forms by bacteria?
Bibliographic record
Abstract
oxyanions, two similar Group 16 chalcogen elements, but with slightly different physicochemical properties that lead to intriguing biological differences. Selenium, Se, is a required trace element compared to tellurium, Te, which is not. Here, the challenges around understanding the uptake transport mechanisms of these anions, as reflected in the model organisms used by different groups, are described. This leads to a discussion around how these oxyanions are subsequently reduced to nanomaterials, which mechanistically, has controversies between ideas around the molecule chemistry, chemical reactions involving reduced glutathione and reactive oxygen species (ROS) production along with the bioenergetics at the membrane versus the cytoplasm. Of particular interest is the linkage of glutathione and thioredoxin chemistry from the cytoplasm through the membrane electron transport chain (ETC) system/quinones to the periplasm. Throughout the opinion review we identify open and unanswered questions about the microbial physiology under selenite and tellurite exposure. Thus, demonstrating how far we have come, yet the exciting research directions that are still possible. The review is written in a conversational manner from three long-term researchers in the field, through which to play homage to the late Professor Claudio Vásquez.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".