Programmed assembly of bespoke prototissues 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. We have designed a microfluidic platform that enables us to build bespoke prototissues from predetermined compositions of two types of protein-polymer protocells. We can accurately control their size, composition and create unique Janus configurations in a way that is not possible with traditional methods. Because we can control the number and type of the protocells that compose the prototissue, we can hence modulate the collective behaviours of this biomaterial. We show control over both the amplitude of thermally induced contractions in the biomaterial and its collective endogenous biochemical reactivity. 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 careful assembly of protocell building blocks of different types. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".