Tissue Level, Distribution and Total Body Selenium Content in Healthy and Diseased Humans in Poland
Bibliographic record
Abstract
The authors obtained tissue samples taken at autopsy from 46 healthy individuals killed in accidents and from 75 corpses of victims of various diseases to analyze selenium levels. The per-weight-unit basis of selenium levels (all expressed as ng/gm wet tissue) in tissues decreased in the following order: kidney (469) > liver > spleen > pancreas > heart > brain > lung > bone > skeletal muscle (51). The highest proportion of body selenium was found in skeletal muscles (27.5%); much less selenium was found in bones (16%) and blood (10%). In the tissues of cancer corpses, the selenium levels were much lower than levels in controls. The lowest selenium levels were found in the livers of alcoholics. Tissue selenium levels found in the study were significantly lower than levels reported in Japan, United States, Canada, and other countries. The low selenium levels in the tissues of Polish residents result from inadequate selenium levels in the soil. The authors used selenium levels in tissues to calculate the amount of selenium in humans in Poland (i.e., approximately 5.2 mg). This level was similar to levels found in New Zealand (i.e., 3.0-6.1 mg), but it was lower than the mean level found in Germany (i.e., 6.6 mg) and in the United States (i.e., 13.0-20.3 mg).
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".