Advanced Tools for Tissue Engineering: Scaffolds, Bioreactors, and Signaling
Bibliographic record
Abstract
This article contains the collective views expressed at the second session of the workshop "Tissue Engineering--The Next Generation,'' which was devoted to the tools of tissue engineering: scaffolds, bioreactors, and molecular and physical signaling. Lisa E. Freed and Farshid Guilak discussed the integrated use of scaffolds and bioreactors as tools to accelerate and control tissue regeneration, in the context of engineering mechanically functional cartilage and cardiac muscle. Edward Guo focused on the opportunities that tissue engineering generates for studies of mechanobiology and on the need for tissue engineers to learn about mechanical forces during tissue and organ genesis. Martha L. Gray focused on the potential of biomedical imaging for noninvasive monitoring of engineered tissues and on the opportunities biomedical imaging can generate for the development of new markers. Robert Tranquillo reviewed the approach to tissue engineering of a spectrum of avascular habitually loaded tissues- blood vessels, heart valves, ligaments, tendons, cartilage, and skin. Jeffrey W. Holmes offered the perspective of a "reverse paradigm''--the use of tissue constructs in quantitative studies of cell-matrix interactions, cell mechanics, matrix mechanics, and mechanobiology. Milica Radisic discussed biomimetic design of tissue-engineering systems, on the example of synchronously contractile cardiac muscle. Michael V. Sefton proposed a new, simple approach to the vascularization of engineered tissues. This session stressed the need for advanced scaffolds, bioreactors, and imaging technologies and offered many enlightening examples on how these advanced tools can be utilized for functional tissue engineering and basic research in medicine and biology.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".