Detection of host and donor cells in sex‐mismatched rat nerve allografts using RT‐PCR for a Y chromosome (H‐Y) marker
Bibliographic record
Abstract
The donor and host source of support cells, such as Schwann cells, in nerve allograft segments have been the subject of debate. The objective of the present study was to assess the utility of a molecular technique that probes for a Y chromosome expressed gene (H-Y) in distinguishing host from donor tissue in sex-mismatched nerve allograft segments. Forty-two Lewis rats received bilateral syngeneic Lewis or allogeneic ACI rat peroneal nerve grafts, with or without cyclosporin A (CsA) treatment. At different times thereafter animals were sacrificed and samples were harvested. We transplanted males and females reciprocally, to study both survival of donor cells (persisting H-Y mRNA in male grafts by transcription polymerase chain reaction (RT-PCR), and graft infiltration by host cells (detectable H-Y mRNA in female grafts). A kinetic analysis revealed a progressive loss of viable donor cells (loss of H-Y mRNA signal) from allografts, beginning 2-3 weeks, and culminating at 4 weeks, with little detectable H-Y in the absence of CsA treatment. CsA treatment led to prolonged survival of allograft cells, confirmed by detectable H-Y mRNA. By studying female grafts in male rats we could confirm that loss of viable donor tissue in allografts was accompanied by infiltration of host (H-Y mRNA positive) cells, whereas no H-Y mRNA signal was seen in males receiving autografts from females or in immunosuppressed allograft segments. These data suggest that reverse RT-PCR analysis for a Y chromosome gene product can be a valuable tool to assess the origin of viable cells in sex-mismatched nerve allotransplantation tissue.
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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 | 0.000 | 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) | 0.001 | 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".