Programmed Assembly of Bespoke Prototissue Spheroids on a Microfluidic Platform
Bibliographic record
Abstract
The precise assembly of protocell building blocks into prototissues that are stable in water, capable of sensing the external environment and which display collective behaviours remains a considerable challenge in prototissue engineering. In this work we explore the use of microfluidic technologies for the programmed assembly of bio-orthogonally reactive protein-polymer protocells into prototissue spheroids of precise size, composition and with unique Janus configurations. We then show that by controlling the number and phenotype of the protocells that compose the prototissue spheroids it is possible to modulate both the amplitude of the thermally induced contractions of the biomaterial and its collective endogenous biochemical reactivity. Overall, our results show that microfluidic technologies enable a new route to the precise and high-throughput fabrication of tissue-like materials with programmable collective properties that can be tuned through a careful assembly of protocell building blocks of different phenotypes. We anticipate that our bespoke prototissues will be a starting point for the development of more sophisticated artificial tissues for use in medicine, soft robotics and environmentally beneficial bioreactor technologies.
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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) | 0.000 | 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) | 0.001 | 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".