Addressing the presence of biogenic selenium nanoparticles in yeast cells: analytical strategies based on ICP-TQ-MS
Bibliographic record
Abstract
Several organisms have demonstrated the ability of synthesising biogenic selenium-containing nanoparticles. Such particles from biological sources have attracted great attention due to several proven activities as antioxidants or antimicrobial agents. However, little is known in terms of size (distribution), shapes, chemical composition and number/amount/concentration of these particles. Therefore, in this work, we proposed the use of complementary analytical strategies that enabled the detection and characterization of selenium-containing nanoparticles in selenized yeast (Saccharomyces cerevisiae). The first strategy to address the intracellular presence of Se within yeast cells, involves the use of single cell ICP-TQ-MS (inductively coupled plasma-mass spectrometry). For this aim, selenium and phosphorous (as constitutive element) were measured as oxides (80Se16O+ and 31P16O+, resp.) in the triple-quadrupole mode. Then, a simple and fast cell lysis by mechanical disruption is conducted (approx. 30 min) in order to prove the presence of selenium-containing nanoparticles (SeNPs). The lysate is analysed by single particle ICP-TQ-MS and, complementarily, by liquid chromatography coupled to ICP-TQ-MS to cover a wider range of particle sizes. One of the samples revealed the presence of dispersed SeNPs with sizes between a few nm and up to 250 nm also confirmed by transmission electron microscopy (TEM) in the form of elemental selenium. The analysis of the certified reference material SELM-1 showed the presence of spherical SeNPs of 4 to 7 nm diameter. These biogenic particles, at least partially, were made of elemental selenium as well. The whole study reveals the excellent capabilities of "single" event ICP-MS methodologies in combination with HPLC-based strategies for a complete characterization of nanoparticulated material in biological samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".