New Frontiers in Biomaterials Research for Tissue Repair and Regeneration
Bibliographic record
Abstract
The field of biomaterials has recently emerged to augment or replace lost or damaged tissues and organs due to the human body's limited ability to self-heal large defects. Historically, metallic components, polymers, ceramics, and composite materials were utilized as synthetic materials along with natural materials to assist in therapy. Various novel biomaterials were developed to respond to a significant amount of new medical challenges in the past decade. Therefore, there is a need to review these newly developed biomaterials and their potential to improve tissue repair and regeneration in a variety of applications. Here, we briefly review the different strategies and attempts to use novel biomaterials, including self-assembled and macromolecular biomaterials, hydrogels, metamaterials, decellularized tissues, and biomaterials obtained via synthetic biology, used either for tissue repair and regeneration or for therapeutic use by exploiting other mechanisms of healing. All these methods aim to create functional materials, devices, systems, and/or organisms with novel and useful functions on the basis of catalogued and standardized biological building blocks. This review details the various methods and introduces the applications of these biomaterials in tissue repair and regeneration, especially for bone, nerve, and skin applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".