Enhancing diaphragmatic defect repair and regeneration: How biomaterials leading the way to progress?
Bibliographic record
Abstract
Abstract Physicians encounter significant challenges in dealing with large diaphragmatic defects in both pediatric and adult populations. Diaphragmatic hernias, such as Morgagni, Bochdalek, and Hiatus hernias, can result in congenital lesions that are often undiagnosed until the appearance of symptoms (bleeding, anemia, and acid reflux). Therefore, substantial potential exists for developing tissue‐engineered constructs as novel therapeutic options in clinics. Recent research indicates promising mid‐term performance for both natural and synthetic materials. However, studies exploring their application in diaphragm regeneration are limited and remain in the early research stages. Additionally, further investigation is required to address the constraints in human tissue supply for clinical implementation. This article comprehensively reviews the role of biomaterials in diaphragmatic tissue repair and regeneration. It emphasizes biomaterials, including biomimetic polymers used in technological solutions. This summary will enable researchers to critically assess the capability of existing natural biomaterials as essential tissue‐engineered patches for clinical use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".