Biomicrofluidic Systems for Hematologic Cancer Research and Clinical Applications
Bibliographic record
Abstract
A persistent challenge in developing personalized treatments for hematologic cancers is the lack of patient specific, physiologically relevant disease models to test investigational drugs in clinical trials and to select therapies in a clinical setting. Biomicrofluidic systems and organ-on-a-chip technologies have the potential to change how researchers approach the fundamental study of hematologic cancers and select clinical treatment for individual patient. Here, we review microfluidics cell-based technology with application toward studying hematologic tumor microenvironments (TMEs) for the purpose of drug discovery and clinical treatment selection. We provide an overview of state-of-the-art microfluidic systems designed to address questions related to hematologic TMEs and drug development. Given the need to develop personalized treatment platforms involving this technology, we review pharmaceutical drugs and different modes of immunotherapy for hematologic cancers, followed by key considerations for developing a physiologically relevant microfluidic companion diagnostic tool for mimicking different hematologic TMEs for testing with different drugs in clinical trials. Opportunities lie ahead for engineers to revolutionize conventional drug discovery strategies of hematologic cancers, including integrating cell-based microfluidics technology with machine learning and automation techniques, which may stimulate pharma and regulatory bodies to promote research and applications of microfluidics technology for drug development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".