Biomimicking design of artificial periosteum for promoting bone healing
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
Background: Periosteum is a vascularized tissue membrane covering the bone surface and plays a decisive role in bone reconstruction process after fracture. Various artificial periosteum has been developed to assist the allografts or bionic bone scaffolds in accelerating bone healing. Recently, the biomimicking design of artificial periosteum has attracted increasing attention due to the recapitulation of the natural extracellular microenvironment of the periosteum and has presented unique capacity to modulate the cell fates and ultimately enhance the bone formation and improve neovascularization. Methods: A systematic literature search is performed and relevant findings in biomimicking design of artificial periosteum have been reviewed and cited. Results: We give a systematical overview of current development of biomimicking design of artificial periosteum. We first summarize the universal strategies for designing biomimicking artificial periosteum including biochemical biomimicry and biophysical biomimicry aspects. We then discuss three types of novel versatile biomimicking artificial periosteum including physical-chemical combined artificial periosteum, heterogeneous structured biomimicking periosteum, and healing phase-targeting biomimicking periosteum. Finally, we comment on the potential implications and prospects in the future design of biomimicking artificial periosteum. Conclusion: : This study provides a holistic view on the design strategy and the therapeutic potential of biomimicking artificial periosteum to promote bone healing. It is hoped to open a new avenue of artificial periosteum design with biomimicking considerations and reposition of the current strategy for accelerated bone healing.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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