Multi-size spheroid formation using microfluidic funnels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it