A handheld bioprinter for multi-material printing of complex constructs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it