A Rapid Automated Method for the Sequential Isolation of CD19, CD3 and CD33 cells from One Tube of Whole Blood. (132.9)
Bibliographic record
Abstract
Abstract Chimerism analysis is typically performed on small blood samples. Analysis of purified cell subsets requires techniques which can isolate >1 cell type from an entire starting sample rather than a divided cell suspension. High cell recovery is essential. Ficoll steps often result in cell loss of 50% while certain lysis and wash steps can affect granulocyte content. We describe a method of sequential selections, starting with 4–5 mL of human whole blood and using a fully automated pipetting robot (RoboSep®). CD19 and CD3 and CD33 positive cell fractions were isolated using immunomagnetic, column-free positive and negative selection (EasySep®). Briefly, cells were first labeled with antibody targeting CD19 positive cells. These were then coupled to magnetic nanoparticles and the sample was placed in a magnet. The supernatant with unlabeled cells was removed to a new tube, leaving isolated CD19 positive cells in the magnet. The supernatant was next labeled with anti-CD3 antibody, magnetic nanoparticles, placed in a magnet and the supernatant was removed to a new tube leaving isolated CD3 positive cells. Finally, a cocktail of antibodies was used to label and remove the remaining unwanted cells (CD33 negative) from the supernatant. The resultant CD33 positive cells were collected in the supernatant fraction leaving CD33 negative cells. Assessment by flow cytometry yielded average purities over 90% for all cell types.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.037 |
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".