Dietary zinc deficiency lowers the proportions of splenic CD90<sup>+</sup>(Thy-1<sup>+</sup>) B-cells and late thymic emigrant T-cells in growing rats
Bibliographic record
Abstract
Zn-deficient (ZD) rats have a lower proportion of splenic CD90+T-cells which could be due to fewer new T-cells exiting the thymus, defective post-thymic maturation or increased cell death. Post-thymic maturation of splenic lymphocytes and their viability were determined by flow cytometry in weanling rats assigned to ZD ( < 1 mg Zn/kg; ad libitum), diet-restricted (DR; 30 mg Zn/kg; limited to the amount of feed as consumed by ZD rats), marginally Zn-deficient (MZD; 10 mg Zn/kg; ad libitum) or control (30 mg Zn/kg; ad libitum) groups for 3 weeks. ZD rats had a 29 % lower percentage of splenic CD90+T-cells and both ZD and DR rats had a 30 % lower proportion of splenic CD90+B-cells compared with control rats. When the splenic CD90+T-cells were characterised further, there was no difference among the groups in the first two stages of post-thymic development; however, ZD, DR and MZD rats had a 42 % lower proportion of late thymic emigrants (TCRalphabeta+CD90+CD45RC+RT6.1+) compared with control rats. There was no difference among groups in the proportion of splenic CD90+T-cells in the non-viable region; however, ZD rats had a higher proportion of CD90+B-cells in the non-viable region compared with MZD and control animals, suggesting that this phenotype was more susceptible to cell death during deficiency. The lower proportion of splenic CD90+T-cells in ZD rats does not appear to be due to a defect in thymic production or increased cell death in the spleen. Future studies should determine if late thymic emigrants have homed to other peripheral organs.
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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 | 0.000 |
| 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".