Multi-size spheroid formation using microfluidic funnels
Bibliographic record
Abstract
We present a microfluidic platform for automatic multi-size spheroid formation within constant volume hanging droplets (HDs) from a single inlet loading of a constant cell concentration. The platform introduces three technological improvements over the existing spheroid formation platforms: 1) cell seeding control is achieved by enrichment of a cell solution rather than dilution; 2) cell seeding in each HD is fully independent and pre-programmable at the design stage; 3) the fabricated chip operates well using a hydrophobic PDMS surface, ensuring long-term storage possibility for device usage. Pre-programmed cell seeding densities at each HD are achieved using a "microfluidic funnel" layer, which has an array of cone-shaped wells with increasing apex angles acting as a metering unit. The integrated platform is designed to form, treat, stain, and image multi-size spheroids on-chip. Spheroids can be analyzed on-chip or easily transferred to conventional well plates for further processing. Empirically, enrichment factors up to 37× have been demonstrated, resulting in viable spheroids of diameters ranging from 230-420 μm and 280-530 μm for OV90 and TOV112D cell lines, respectively. We envision that microfluidic funnels and single inlet multi-size spheroid (SIMSS) chips will find broad application in 3D biological assays where size-dependent responses are expected, including chemoresponse assays, photodynamic therapy assays, and other assays involving drug transport characterization in drug discovery.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".