Development and evaluation of two whole-blood flow cytometry protocols for monitoring patients treated with JAK inhibitors
Bibliographic record
Abstract
Abstract Introduction The clinical efficacy of Janus kinase inhibitors (JAKinibs) is highly variable and their safety profiles are poorly understood. Methods We established two flow cytometry panels for the assessment of two promising leukocyte biomarkers: signal transducer and activator of transcription (STAT) phosphorylation and cytokine receptor expression. We evaluated the first panel, which assesses phosphorylation levels for STAT1, STAT3, and STAT5 after cytokine stimulation, with or without in vitro pretreatment with JAKinibs, in 10 healthy donors. We then evaluated the second panel, which assesses cytokine receptor expression on T cells and B cells, in five healthy donors. Results Stimulation with interleukin (IL)-2 or IL-7 increased STAT5 phosphorylation in T cells but not in B cells or monocytes. IL-6 stimulation induced STAT3 phosphorylation in monocytes and CD4 T cells and, to a lesser extent, in CD8 T cells, but not in B cells. IL-21 stimulation led to STAT3 phosphorylation in T cells and, to a lesser extent, in B cells, but not in monocytes. Interferon-α stimulation increased STAT1 phosphorylation in all cell types. STAT phosphorylation levels were lower after pretreatment with JAKinibs. A dose–response curve was plotted, confirming the correlation between JAKinib concentration and STAT phosphorylation inhibition. The second panel showed that each cell type displayed a distinct pattern of cytokine receptors expression, and that this pattern might be modified by in vitro treatment with JAKinibs. Conclusion This preliminary study confirms the utility of flow cytometry for monitoring the biological effects of JAKinibs. Further studies on treated patients are now required to evaluate the clinical value of these two flow cytometry panels.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".