A specialized tube to make enrichment of specific cell subsets faster and easier (124.3)
Bibliographic record
Abstract
Abstract Many experimental protocols require the enrichment of specific cell subsets from peripheral blood. RosetteSep™ cell enrichment and standard mononuclear cell (MNC) preparation both involve density gradient centrifugation, which entails slowly layering the sample over the density gradient medium to avoid mixing, and carefully pipetting to remove the enriched cells after centrifugation. Centrifugation must be performed with the brake off to avoid disturbing the enriched cell layer, further lengthening the process. SepMate™, a centrifugation tube with a specialized insert, was developed to allow rapid layering of the sample onto the density gradient medium, and pouring off of the enriched cells after centrifugation, thus simplifying the entire process. When using SepMate™, the cocktail incubation time and centrifugation time could each be shortened to 10 min, making RosetteSep™ cell enrichment even faster. RosetteSep™ enrichments of mononuclear cell subsets using the SepMate™ tubes and protocol gave equivalent purity and recovery of desired cells compared to using the standard RosetteSep™ protocol, and desired cells could be enriched from whole blood in <30 min. Purities of specific cell types were: CD3 T Cells 96 ± 1 (n=5), CD4 T Cells 94 ± 5 (n=3), CD8 T Cells 85 ± 11 (n=4), B Cells 92 ± 6 (n=3), NK Cells 85 ± 5 (n=5), monocytes 68 ± 8 (n = 4). The protocol is easily scalable to process multiple samples simultaneously, and the SepMate™ tube can also be used to prepare MNCs.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.040 |
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".