Selenium Distribution and Speciation in Tissues from Rats Administered with Non-Native Selenotrisulfides
Bibliographic record
Abstract
Selenotrisulfides (STS, R–S–Se–S–R) are metabolic intermediates in the bioconversion of inorganic Se species to organoselenium compounds. These Se species are reactive with a variety of endogenous molecules, particularly thiol-containing proteins, with this reactivity facilitating Se transport and subsequent utilization within the body. In this study, X-ray fluorescence microscopy (XFM) and high energy resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XAS) were applied to investigate Se distribution and speciation in vivo. Male rats administered with 1 mg Se/kg b.w. as selenious acid (SA), L-penicillamine selenotrisulfide (PenSSeSPen) or selenenyl penicillamine bound to rat serum albumin (RSA-SSeSPen) showed statistically significant elevations in Se concentrations in the kidney, liver, and blood after 48 h treatment; however, no change in Se concentration was observed in the testes. Notably, XFM revealed a strong colocalization of Se and Cu in the renal cortex, a phenomenon previously observed in cultured cells and in rats fed diets supplemented with 5 mg Se/kg as selenite. Linear combination and principal component analyses of Se Kα 1 HERFD-XAS spectra revealed marked differences in Se speciation between the renal cortex and medulla and between red blood cells and plasma for all groups, including the control. STS were identified in linear combination fits of spectra from all tissues, except the testes. These results highlight the vital roles of STS in the intracellular reduction and transport of Se throughout the bloodstream and various tissues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".