An integrated microfluidic flow-focusing platform for on-chip fabrication and filtration of cell-laden microgels
Bibliographic record
Abstract
We present the development of a stable continuous, and integrated microfluidic platform for the high-throughput fabrication of monodisperse cell-laden microgel droplets with high and maintained cellular viability. This is through combining onto one chip all the required processes from the droplet generation in a flow focusing microfluidic junction passing through on-chip photocrosslinking to the separation of the droplets from the continuous oil phase. To avoid cellular aggregation during the droplet generation process, cells were treated with bovine serum albumin (BSA) before mixing with gelatin methacrylate (GelMA). And, a magnetic mixer was applied to the GelMA prepolymer-cell suspension syringe to eliminate cell sedimentation. These approaches resulted in having a reasonable distribution of cells among monodisperse microdroplets. The microdroplets were irradiated with a 405 nm wavelength laser beam while passing through the crosslinking chamber of the microfluidic device. The produced microgels enter the filtration unit of the same device where they were gently separated from the oil phase into the washing buffer aqueous solution of Tween 80 using the filter microposts array. The viability of the encapsulated cells was around 85% at day 1 and was maintained throughout 5 days. Using this method of controlling cell encapsulation with on-chip crosslinking and oil filtration, highly efficient cell-laden microgel production is achieved. The presented integrated microfluidic platform can be a candidate for standard cell-encapsulation experiments and other tissue engineering applications.
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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 | 0.000 | 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) | 0.001 | 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".