Mechanical segregation and capturing of clonal circulating plasma cells in multiple myeloma using micropillar-integrated microfluidic device
Bibliographic record
Abstract
Multiple myeloma (MM), the disorder of plasma cells, is the second most common type of hematological cancer and is responsible for approximately 20% of deaths from hematological malignancies. The current gold standard for MM diagnosis includes invasive bone marrow aspiration. However, it lacks the sensitivity to detect minimal residual disease, and the nonuniform distribution of clonal plasma cells (CPCs) within bone marrow also often results in inaccurate reporting. Serum and urine assessment of monoclonal proteins, such as Kappa light chains, is another commonly used approach for MM diagnosis. Although it is noninvasive, the level of paraprotein elevation is still too low for detecting minimal residual disease and nonsecretive MM. Circulating CPCs (cCPCs) have been reported to be present in the peripheral blood of MM patients, and high levels of cCPCs were shown to correlate with poor survival. This suggests a potential noninvasive approach for MM disease progress monitoring and prognosis. In this study, we developed a mechanical property-based microfluidic platform to capture cCPCs. Using human myeloma cancer cell lines spiked in healthy donor blood, the microfluidic platform demonstrates high enrichment ratio (>500) and sufficient capture efficiency (40%-55%). Patient samples were also assessed to investigate the diagnostic potential of cCPCs for MM by correlating with the levels of Kappa light chains in patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".