The microwell-mesh: A novel device and protocol for the high throughput manufacturing of cartilage microtissues
Bibliographic record
Abstract
Microwell platforms are frequently described for the efficient and uniform manufacture of 3-dimensional (3D) multicellular microtissues. Multiple partial or complete medium exchanges can displace microtissues from discrete microwells, and this can result in either the loss of microtissues from culture, or microtissue amalgamation when displaced microtissues fall into common microwells. Herein we describe the first microwell platform that incorporates a mesh to retain microtissues within discrete microwells; the microwell-mesh. We show that bonding a nylon mesh with an appropriate pore size over the microwell openings allows single cells to pass through the mesh into the microwells during the seeding process, but subsequently retains assembled microtissues within discrete microwells. To demonstrate the utility of this platform, we used the microwell-mesh to manufacture hundreds of cartilage microtissues, each formed from 5 × 10(3) bone marrow-derived mesenchymal stem/stromal cells (MSC). The microwell-mesh enabled reliable microtissue retention over 21-day cultures that included multiple full medium exchanges. Cartilage-like matrix formation was more rapid and homogeneous in microtissues than in conventional large diameter control cartilage pellets formed from 2 × 10(5) MSC each. The microwell-mesh platform offers an elegant mechanism to retain microtissues in microwells, and we believe that this improvement will make this platform useful in 3D culture protocols that require multiple medium exchanges, such as those that mimic specific developmental processes or complex sequential drug exposures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".