A semi-nested real-time PCR method to detect low chimerism percentage in small quantity of hematopoietic stem cell transplant DNA samples
Bibliographic record
Abstract
Chimerism status evaluation of post-allogeneic hematopoietic stem cell transplantation samples is essential to predict post-transplant relapse. The most commonly used technique capable of detecting small increments of chimerism is quantitative real-time PCR. Although this method is already used in several laboratories, previously described protocols often lack sensitivity and the amount of the DNA required for each chimerism analysis is too high. In the present study, we compared a novel semi-nested allele-specific real-time PCR (sNAS-qPCR) protocol with our in-house standard allele-specific real-time PCR (gAS-qPCR) protocol. We selected two genetic markers and analyzed technical parameters (slope, y-intercept, R2, and standard deviation) useful to determine the performances of the two protocols. The sNAS-qPCR protocol showed better sensitivity and precision. Moreover, the sNAS-qPCR protocol requires, as input, only 10 ng of DNA, which is at least 10-fold less than the gAS-qPCR protocols described in the literature. Finally, the proposed sNAS-qPCR protocol could prove very useful for performing chimerism analysis with a small amount of DNA, as in the case of blood cell subsets.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".