Soluble CD23 measurement by CBA: A convenient and reliable quantification method in Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
The soluble form of the transmembrane glycoprotein CD23 corresponding to the low-affinity receptor for the immunoglobulin E (sCD23) is found in the serum of patients with chronic lymphocytic leukemia (CLL). In this disease, an increase in sCD23 level is predictive of poor prognosis at diagnosis as well as during clinical outcome. Quantification of sCD23 is classically performed by ELISA assay, a method not routinely used in hematology laboratories. Our aim was to apply cytometric bead array (CBA) technology to measure sCD23 levels. We tested 420 serum samples, 360 from patients and 60 from healthy volunteers. We selected 3 pairs of monoclonal antibodies (moAb) recognizing the CD23 molecule that were tested in various conditions of temperature, centrifugation, washing or chemical supplementation. Satisfactory performances in terms of repeatability (CV: 5%) and reproducibility (CV: 6%) were obtained with the selected pair of antibodies, with a threshold of positivity at 6 ng/mL. CBA and ELISA techniques were correlated with a Spearman coefficient at 0.99. The reproducibility and reliability of the sCD23 CBA assay were confirmed, with a Spearman coefficient at 0.99 in a series of 23 CLL patients and 13 controls tested in 2 laboratories equipped with different cytometers and using different lots of CBA reagents. Data obtained with serum and plasma samples were correlated with a Spearman coefficient at 0.99. Our study validates a simple method that allows the clinicians to benefit from an indicator of prognosis at the diagnosis as well as a marker of the evolution of CLL disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".