Evaluation of Peripheral Blood Leukocyte Subsets in Systemic Lupus Erythematosus (SLE) Enabled by a Microfluidic Cell Separator
Bibliographic record
Abstract
Abstract Although granulocyte signatures have been associated with SLE severity, their role in SLE has been poorly understood due to the suboptimal methods for granulocyte recovery from peripheral blood. An automated microfluidic technology (Sorterra™ prototype) was used to isolate all leukocytes with minimal platelet (PLT) or red blood cell (RBC) contamination to assess the cell subsets. Leukocytes were isolated from peripheral blood collected from healthy donors (HD) and SLE patients (3 female, 3 male in each group) using Sorterra, RBC lysis, or Ficoll®. Viability and recovery of each leukocyte subset was evaluated by cell counter and flow cytometry. Sorterra recovered 92±4% of leukocytes from HD and SLE samples with negligible RBC/PLT/microparticle (MP) contamination, whereas Ficoll isolated 35±15% of leukocytes, losing nearly all the granulocytes and retaining a substantial level of RBC/PLT/MP. With RBC lysis, up to 65% of neutrophils and 41% of eosinophils were lost in the process. Sorterra recovered more monocytes (~80%) than Ficoll (p<0.05). The recovery of lymphocytes by Sorterra (~90%) was not different from Ficoll or RBC lysis. The recovery of T and B cell subsets, NK, and NKT cells, was also not different among the isolation methods. The viability of Sorterra isolated leukocytes was significantly higher than the Ficoll isolated cells. The Sorterra prototype was superior to RBC lysis and Ficoll in recovering total leukocytes with high viability and ease of use and provided a more consistent leukocyte isolate from HD and SLE patients than other methods. The ability to recover granulocytes in their native state is unmatched by existing technologies and enables future studies to better elucidate the role of granulocytes in SLE development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".