Architecture of an All‐In‐One Microfluidic Platform for Accelerated Cancer Seeding, Enhanced Spheroid Formation, and Dynamic Drug Screening Trials
Bibliographic record
Abstract
ABSTRACT Effective cancer drug screening requires platforms that replicate the physiological complexity of tumor microenvironments. Traditional 2D systems fail to model critical features such as hypoxic gradients, interstitial flow, and drug penetration dynamics, resulting in limited predictive accuracy. To address these challenges, a microfluidic platform integrating porous V‐slanted hydrogel microwells, capable of forming uniform 3D cancer spheroids within the physiologically relevant size range of 200–500 µm, while enabling dynamic flow‐based drug testing is needed. This platform introduces advection‐based dynamic flow through a double‐layered microfluidic design, overcoming reliance on static diffusion seen in conventional systems. Dynamic drug screening performed on this platform demonstrates enhanced drug penetration and more consistent therapeutic responses compared to static conditions, emphasizing the importance of physiological flow in replicating in vivo tumor behavior. By controlling cross‐flow conditions, the platform ensures uniform drug delivery and enables a more reliable assessment of therapeutic efficacy, addressing variability often observed in static and 2D systems. Moreover, compatibility with high‐throughput applications makes it a scalable, robust solution for preclinical drug testing. This work highlights the critical role of dynamic environments in cancer drug screening, offering an improved physiologically relevant approach for studying drug responses and advancing personalized medicine strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".