Bibliographic record
Abstract
This chapter provides a comprehensive examination of in situ bioprinting, a promising technology that is poised to revolutionize biomedical research. Its potential lies in the precise deposition of biomaterials directly onto a target site, external or internal, effectively enhancing throughput repair while mitigating the risk of tissue contamination. The chapter explores the multifaceted advantages of in situ bioprinting, including precision in cellular deposition, enhanced integration with host tissue, fidelity in replicating the extracellular environment, and its potential in personalized medicine. It elaborates on various in situ bioprinting technologies, with a focus on automated, semi-automated, and handheld bioprinters. It discusses the nuances of engineering bioinks and biomaterials, considering crosslinking, biocompatibility, and mechanical properties. We further underscore the application of in situ approaches in regenerating various tissues including cartilage, bone, skeletal muscle, skin, and dental pulp. While acknowledging challenges such as vascularization control, high costs, and complexities in cell deposition, the chapter points to the exciting future directions of 4D bioprinting, open-source interdisciplinary collaboration, and machine learning integration. This chapter serves as an engaging read for those intrigued by the cutting-edge field of in situ bioprinting and its transformative role in medical applications.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".