RETRACTED: Precision Isolation of Circulating Leukemia Cells in Chronic Myelogenous Leukemia Patients Using a Novel Microfluidic Device and Its Clinical Applications
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Chronic Myelogenous Leukemia (CML) is a prevalent hematologic malignancy characterized by the malignant transformation of myeloid cells and their proliferation in the peripheral blood. The management of CML poses significant challenges, particularly in detecting and eradicating minimal residual disease, which is crucial for preventing relapse and improving survival outcomes. Traditional minimal residual disease detection methods, such as bone marrow aspiration, are invasive and have limitations which include the potential for sampling errors and false negatives. This study introduces a novel label-free microfluidic chip designed for the segregation and recovery of circulating leukemia cells, offering a non-invasive liquid biopsy approach with potential applications in precision medicine. Over July 2021 to October 2023, we recruited 56 CML patients across various disease stages and collected blood samples for analysis using our microfluidic device. The device demonstrated high efficacy in isolating circulating leukemia cells, with an optimal capture efficiency of 78% at a sample flow rate of 3 mL/h. Our results indicate that the microfluidic device can efficiently segregate and quantify circulating leukemia cells, providing a detailed understanding of CML progression and treatment response. The significant reduction in circulating leukemia cell counts in patients in complete remission highlights the device's potential in monitoring treatment efficacy. Furthermore, the device's sensitivity in detecting minimal residual disease could offer a more reliable prognostic tool for therapeutic decision-making in CML management.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".