Impact of Biogenic and Chemogenic Selenium Nanoparticles on Model Eukaryotic Lipid Membranes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microbial nanotechnology is an expanding research area devoted to producing biogenic metal and metalloid nanomaterials (NMs) using microorganisms. Often, biogenic NMs are explored as antimicrobial, anticancer, or antioxidant agents. Yet, most studies focus on their applications rather than the underlying mechanism of action or toxicity. Here, we evaluate the toxicity of our well-characterized biogenic selenium nanoparticles (bSeNPs) produced by the Stenotrophomonas maltophilia strain SeITE02 against the model yeast Saccharomyces cerevisiae comparing it with chemogenic SeNPs (cSeNPs). Knowing from previous studies that the biogenic extract contained bSeNPs in an organic material (OM) and supported here by Fourier transform infrared spectroscopy, we removed and incubated it with cSeNPs (cSeNPs_OM) to assess its influence on the toxicity of these formulations. Specifically, we focused on the first stages of the eukaryotic cell exposure to these samples─i.e., their interaction with the cell lipid membrane, which was mimicked by preparing vesicles from yeast polar lipid extract or phosphatidylcholine lipids. Fluidity changes derived from biogenic and chemogenic samples revealed that the bSeNP extract mediated the overall rigidification of lipid vesicles, while cSeNPs showed negligible effects. The OM and cSeNPs_OM induced similar modifications to the bSeNP extract, reiterating the need to consider the OM influence on the physical–chemical and biological properties of bSeNP extracts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".