lmmunohistochemical detection of metallothionein in liver, duodenum and kidney after dietary copper-overload in rats
Bibliographic record
Abstract
Metallothionein (MT) has been used in \nimmunohistochemical techniques to indicate presence \nand distribution of heavy metals within biological \ntissues. This study describes a comparison of the pattern \nof MT-immunostaining in the liver, duodenum and \nkidney during dietary copper overload in rats. Sixteen \nmale 10-week-old Wistar rats were randomly allocated \ninto groups of four. Two groups were fed a pelleted diet \ncontaining 1,500 mgíkg copper and two control groups \nreceived a rodent diet containing 10 mgkg copper. After \n6 weeks samples of liver, kidney and duodenum were \ncollected for immunohistochemistry and histology. An \nindirect immunoperoxidase technique, using monoclonal \nantibody E9 against horse MT and polyclonal sera \nagainst rabbit MT, was employed. Copper-loaded rats \nhad marked MT-immunoreactivity within the nucleus \nand cytoplasm of many periportal hepatocytes, renal \nproximal convoluted tubule epithelial cells, intestinal \ncolumnar epithelial cells and Paneth cells. Immunohistochemical \nstaining was similar using either mouse \nanti-MT polyclonal serum, or monoclonal antibody E9. \nHepatocytes surrounding inflammatory foci were \npositive for MT, supporting the proposed role of this \nprotein in free radical scavenging. The presence of MTexcretion o€ copper-metallothionein (Cu-MT) in copperloaded \nrats. Paneth cells were easily detected using MTimmunostaining. \nMT may play a part in absorption of \ncopper from intestinal contents and possible storage as \nCU-MT in Paneth cells. The function of Paneth cells \nremains unknown but the presence of marked MTimmunoreactivity \nin these cells, observed in copperloaded \nrats, suggests their involvement in homeostasis \nand metabolism o€ copper. \nin the kidney appears to be associated with renal
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".