The role of tissue engineering and regenerative medicine in the treatment of sport injuries a review study
Bibliographic record
Abstract
Managing sports injuries is clinically challenging. Although new techniques can delay musculoskeletal deterioration and promote tissue restoration, they are not widely used. Thus, there is a critical need to promulgate these new methods. In recent years, “tissue engineering” approaches have been developed for improving the regeneration of cartilage by transplanting cells or engineered constructs into injured tissue. The mechanical environment affects the biology of a tissue and is necessary for the development and maintenance of load-bearing tissues. Platelets can be combined with other healing factors as a new therapeutic modality. Platelet-rich plasma (PRP) can be introduced as an autologous blood product that may improve wound healing. In this regard, stem cell therapy that focuses on mesenchymal stem cells (MSCs) has been proposed as a new treatment method in sports medicine. MSCs are multipotent; they have the ability to differentiate into other cells, notably osteoblasts, chondrocytes, adipocytes, myoblasts, and fibroblasts, depending on a variety of factors. In summary, recent advances in tissue regeneration have provided new perspectives for the use of tissue engineering to enhance tissue healing after sports injuries namely the microfracture method, the mechanical stimuli method, PRP therapy, and stem cell therapy
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".