Advanced Tools for Tissue Engineering: Scaffolds, Bioreactors, and Signaling
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
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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