Editorial: Polydopamine-based structures innovation for surface engineering and Musculoskeletal Tissue Regeneration
Bibliographic record
Abstract
Polydopamine-Based Structures Innovation for Surface Engineering and Musculoskeletal Tissue RegenerationMusculoskeletal disorders (MSDs) lead to a costly challenge for patients.The conditions range from temporary impairments to those that result in permanent disability and functional limitations (Musculoskeletal health, 2022).According to Global Burden of Disease 2019 data, approximately 1.71 billion people in the world suffer from MSDs, such as back/neck pain, fractures, osteoarthritis, and rheumatoid arthritis (Cieza et al., 2020).People with MSDs generally have limited mobility and reduced functioning, which restricts their ability to integrate into society.The regeneration of MSD following surgical interventions or prostheses has been problematic due to size dependency, fibrocartilage tissue formation, and poor biomechanical restoration (Glyn-Jones et al., 2015;Liu et al., 2019).It is therefore becoming increasingly promising to use tissue and surface engineering strategies to treat MSDs, using bio-inspired polymers as building blocks (Berthiaume and Yarmush, 2003).However, translation of research findings into practice still remains a challenge.Bioinspired polymers are an advantageous choice when it comes to the development of MSD regeneration.Over the last few decades, it has been discovered that dopamine, a hormone and neurotransmitter, can also be used in polymeric form (under spontaneous oxidative polymerization) in tissue and surface engineering applications (Ghalandari et al., 2021).Polydopamine (PDA) effectiveness has expanded its application in bioengineering as nanoparticles and carriers.This melanin-like material is also used for regenerating bone, cartilage, muscle, nerves, and tendons due to its bioactivity, hydrophilicity, bioadhesion, and thermal stability.PDA's success in tissue engineering field is attributed to its capacity to regulate tissue and cellular responses, including cell adhesion, proliferation, and to initiate repair and immune responses.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".