Antimicrobial activity of biogenically produced spherical Se‐nanomaterials embedded in organic material against <i>Pseudomonas aeruginosa</i> and <i>Staphylococcus aureus</i> strains on hydroxyapatite‐coated surfaces
Bibliographic record
Abstract
Summary In an effort to prevent the formation of pathogenic biofilms on hydroxyapatite ( HA )‐based clinical devices and surfaces, we present a study evaluating the antimicrobial efficacy of S pherical biogenic Se‐ N anostructures Em bedded in O rganic material (Bio Se‐ NEMO ‐S) produced by Bacillus mycoides Sel TE 01 in comparison with two different chemical selenium nanoparticle (Se NP ) classes. These nanomaterials have been studied as potential antimicrobials for eradication of established HA ‐grown biofilms, for preventing biofilm formation on HA ‐coated surfaces and for inhibition of planktonic cell growth of Pseudomonas aeruginosa NCTC 12934 and Staphylococcus aureus ATCC 25923. Bio Se‐ NEMO resulted more efficacious than those chemically produced in all tested scenarios. Bio Se‐ NEMO produced by B. mycoides Sel TE 01 after 6 or 24 h of Na 2 SeO 3 exposure show the same effective antibiofilm activity towards both P. aeruginosa and S. aureus strains at 0.078 mg ml −1 (Bio Se‐ NEMO 6 ) and 0.3125 mg ml −1 (Bio Se‐ NEMO 24 ). Meanwhile, chemically synthesized Se NP s at the highest tested concentration (2.5 mg ml −1 ) have moderate antimicrobial activity. The confocal laser scanning micrographs demonstrate that the majority of the P. aeruginosa and S. aureus cells exposed to biogenic Se NP s within the biofilm are killed or eradicated. Bio Se‐ NEMO therefore displayed good antimicrobial activity towards HA ‐grown biofilms and planktonic cells, becoming possible candidates as new antimicrobials.
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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".