Extrusion and Microfluidic‐Based Bioprinting to Fabricate Biomimetic Tissues and Organs
Bibliographic record
Abstract
Next generation engineered tissue constructs with complex and ordered architectures aim to better mimic the native tissue structures, largely due to advances in three-dimensional (3D) bioprinting techniques. Extrusion bioprinting has drawn tremendous attention due to its widespread availability, cost-effectiveness, simplicity, and its facile and rapid processing. However, poor printing resolution and low speed have limited its fidelity and clinical implementation. To circumvent the downsides associated with extrusion printing, microfluidic technologies are increasingly being implemented in 3D bioprinting for engineering living constructs. These technologies enable biofabrication of heterogeneous biomimetic structures made of different types of cells, biomaterials, and biomolecules. Microfluiding bioprinting technology enables highly controlled fabrication of 3D constructs in high resolutions and it has been shown to be useful for building tubular structures and vascularized constructs, which may promote the survival and integration of implanted engineered tissues. Although this field is currently in its early development and the number of bioprinted implants is limited, it is envisioned that it will have a major impact on the production of customized clinical-grade tissue constructs. Further studies are, however, needed to fully demonstrate the effectiveness of the technology in the lab and its translation to the clinic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".