A handheld bioprinter for multi-material printing of complex constructs
Bibliographic record
Abstract
Abstract In situ bioprinting—the process of depositing bioinks at a defected area, has recently emerged as a versatile technology for tissue repair and restoration via site-specific delivery of pro-healing constructs. The ability to print multiple materials in situ is an exciting approach that allows simultaneous or sequential dispensing of different materials and cells to achieve tissue biomimicry. Herein, we report a modular handheld bioprinter that deposits a variety of bioinks in situ with exquisite control over their physical and chemical properties. Combined stereolithography 3D printing and microfluidic technologies allowed us to develop a novel low-priced handheld bioprinter. The ergonomic design of the handheld bioprinter facilitate the shape-controlled biofabrication of multi-component fibers with different cross-sectional shapes and material compositions. Furthermore, the capabilities of the produced fibers in the local delivery of therapeutic agents was demonstrated by incorporating drug-loaded microcarriers, extending the application of the printed fibers to on-demand, temporal, and dosage-control drug delivery platforms. Also, the versatility of this platform to produce biosensors and wearable electronics was demonstrated via incorporating conductive materials and integrating pH-responsive dyes. The handheld printer’s efficacy in generating cell-laden fibers with high cell viability for site-specific cell delivery was shown by producing single-component and multi-component cell-laden fibers. In particular, the multi-component fibers were able to model the invasion of cancer cells into the adjacent tissue.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".