Segregation, Capture and Recovery of Circulating Clonal Plasma Cells in Peripheral Blood of MM patients using Micropillar-integrated Microfluidic Device and its Cinical Applicability
Bibliographic record
Abstract
Multiple myeloma (MM), the disorder of plasma cells, is the second most commonly seen hematological cancer. 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 within bone marrow often results in inaccurate reporting. Serum and urine assessment of monoclonal proteins is another commonly used approach for MM diagnosis. Although non-invasive, the level of paraprotein elevation is too low for detecting minimal residual disease and non-secretive MM. Circulating CPCs (cCPCs) have been reported to be present in the peripheral blood of MM patients and is emerged as an important biomarker in evaluating the Minimal Residual Disease status in MM. The elevation of cCPCs in patients is closely related to the recurrence of the disease. Compared to the conventional bone marrow aspirate, it can be accessed regularly in a non-invasive manner. Over the past decade, microfluidic techniques have been widely explored as a platform to segregate the rare cells in blood with the advantages of easy manipulation and relatively low cost. In this thesis, we present a novel mechanical property-based microfluidic platform to segregate cCPCs and successfully demonstrate its clinical applicability in MM. First, we developed a microfluidic device based on the unique physical and mechanical properties of MM cells. The prototype was testified by spiking human myeloma cell lines in healthy donor blood, and the key parameters such as enrichment ratio and capture efficiency were also evaluated. Next, we used our developed microfluidic platform to explore its clinical utility in comparison with existing methods of Multiparameter Flow Cytometry and Serum Protein Electrophoresis analysis. MM blood samples of different disease stages were processed through the microfluidic device. In addition, we conducted longitudinal studies on patients, monitoring the disease status before and after treatment. We recorded cCPCs level detected by microfluidic chip and compared it in parallel with the paraprotein level detected by serum protein electrophoresis. Furthermore, the ability to retrieve the cCPCs from microfluidic chip was also demonstrated and the subsequent cytogenetic analysis was performed. The MM-related genetic mutations were identified.
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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 | 0.001 | 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 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".