Determination of selenium-containing species, including nanoparticles, in selenium-enriched Lingzhi mushrooms
Bibliographic record
Abstract
Mushrooms are considered a valuable food source due to their high protein and fibre and low fat content, among the other health benefits of their consumption. Selenium is an essential nutrient and is renowned for its chemo-preventative properties. In this study, batches of selenium-enriched Lingzhi mushrooms were prepared by growing mycelium and fruit in substrates containing various concentrations of sodium selenite. The mushroom fruit accumulated low levels of selenium with selenomethionine being the most abundant form in all enriched samples. Conversely, the mycelium showed significant selenium accumulation but relatively low proportions of selenomethionine. The red colour of the selenium-enriched mycelia indicated the probable presence of selenium nanoparticles, which was confirmed by single-particle inductively coupled plasma-mass spectrometry. Mean particle diameters of 90-120 nm were observed, with size distributions of 60-250 nm. Additional analysis with transmission electron microscopy confirmed this size distribution and showed that the biogenic selenium nanoparticles were roughly spherical in shape and contained elemental selenium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".