Biogenic selenium and tellurium nanoparticles synthesized by environmental microbial isolates efficaciously inhibit bacterial planktonic cultures and biofilms
Bibliographic record
Abstract
The present study deals with Se(0)- and Te(0)-based nanoparticles bio-synthesized by two selenite- and tellurite-reducing bacterial strains, namely Stenotrophomonas maltophilia SeITE02 and Ochrobactrum sp. MPV1, isolated from polluted sites. We evidenced that, by regulating culture conditions and exposure time to the selenite and tellurite oxyanions, differently sized zero-valent Se and Te nanoparticles were produced. The results revealed that these Se(0) and Te(0) nanoparticles possess antimicrobial and biofilm eradication activity against Escherichia coli JM109, Pseudomonas aeruginosa PAO1, and Staphylococcus aureus ATCC 25923. In particular, Se(0) nanoparticles exhibited antimicrobial activity at quite low concentrations, below that of selenite. Toxic effects of both Se(0) and Te(0) nanoparticles can be related to the production of reactive oxygen species upon exposure of the bacterial cultures. Evidence so far achieved suggests that the antimicrobial activity seems to be strictly linked to the dimensions of the nanoparticles: indeed, the highest activity was shown by nanoparticles of smaller sizes. In particular, it is worth noting how the bacteria tested in biofilm mode responded to the treatment by Se(0) and Te(0) nanoparticles with a susceptibility similar to that observed in planktonic cultures. This suggests a possible exploitation of both Se(0) and Te(0) nanoparticles as efficacious antimicrobial agents with a remarkable biofilm eradication capacity.
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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) | 0.000 | 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".