A Quantitative Polymerase Chain Reaction Test to Enumerate Leukocytes in Allograft Tissue and the Implications for Donor Eligibility Testing
Bibliographic record
Abstract
A country-to-country analysis of infectious disease screening requirements for donated tissues or cells reveals they are not often harmonized. Transmission of one such infectious disease, human T-lymphotropic virus (HTLV), is related to the transfer of HTLV-infected, viable leukocytes of sufficient number. The ability to characterize allograft tissue as being absent of leukocytes, or containing relatively few leukocytes, by using a specific test has not been previously investigated. A quantitative polymerase chain reaction (qPCR) test was developed to interrogate protein tyrosine phosphatase, receptor type C (PTPRC) gene expression in tissue samples and was able to determine the number of leukocytes present in a tissue. The impact of a qualified leukocyte tissue testing method should be significant and lead to changes in donor eligibility regulations in certain countries. Human leukapheresis samples were used as a control to establish the amount of PTPRC in leukocytes. That value was used as a comparator to determine the number of leukocyte equivalents in tissues of interest. The qPCR test measured tissue leukocyte equivalents and the results were consistent with the relative abundance of leukocytes predicted for each tissue. Using qPCR to calculate leukocyte equivalents based upon PTPRC gene expression can be successfully employed to estimate the number of leukocytes in a tissue or allograft. This method could be used as a screen to rule out tissues that do not meet the criteria of being leukocyte rich and, therefore, do not need direct HTLV testing.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".