Bibliographic record
Abstract
Abstract Monitoring the change of cell populations within patients after hematopoietic stem cell transplant (HSCT) is crucial for determining the success of treatment. With current studies largely focused on determining mixed chimerism of nucleated cells, mixed chimerism for non-nucleated red blood cell (RBC) populations was rarely studied. In this study, based on the differences between donor and recipient ABO blood group surface markers and using commercially available mouse monoclonal antibodies (mAbs) for anti-A 1 and anti-glycophorin A (GlyA), a flow cytometry (FCM) based assay was tested to monitor RBC chimerism for post-HSCT patients with combinations A 1 /O, A 1 B/O, A 1 /B, and A 1 B/B blood group difference. Titration curves of mixed blood type combinations with 10% margins were made. These titration curves had a consistent R 2 value > 0.99 and a standard deviation (SD) value < 5%. This suggests that the proposed assay was valid, precise, and reliable. Furthermore, a patient sample with recipient A and donor O mixed blood type was tested and showed that the proposed assay was accurate. Since anti-A 2 and anti-B mAbs were not tested, next steps would be testing these antibodies such that the assay can monitor post-HSCT patients with combinations A 2 /O, A 2 B/O, A 2 /B, A 2 B/B, AB/B, and B/O blood type difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".