Critical advances in biofabrication and biomaterial strategies in tracheal tissue engineering: A comprehensive overview
Bibliographic record
Abstract
The trachea is a vital respiratory organ that connects the larynx to the lungs and performs crucial functions. Various conditions can cause severe and often irreversible damage to individuals trachea of all age groups. Tracheal regeneration remains a major challenge in respiratory medicine, requiring a innovative solutions to address various underlying causes. Existing clinical interventions often have significant limitations and associated complications. Tissue engineering has potential, but its effectiveness has been limited due to challenges such as poor durability and insufficient revascularization. This review aims to provide a comprehensive exploration of the landscape of tracheal regeneration, shedding light on the path towards advancements in addressing extensive tracheal defects. It follows a structured approach, introducing various surgical procedures, along with their associated complications. Subsequently, it delves into the myriad biomaterials investigated in the realm of tracheal tissue engineering, emphasizing the significance of design considerations in scaffold fabrication. The review then navigates through various platforms utilized in tracheal tissue engineering and recent innovative approaches employed in this domain. Additionally, it provides insights into the clinical translation of tissue-engineered trachea, highlighting recent advancements and challenges encountered in real-world applications. Finally, it discusses the significant challenges and offers a perspective outlook on the future of tracheal tissue engineering. Addressing current limitations and envisioning novel strategies, the review contributes to the ongoing dialogue and progression in this critical field of regenerative medicine.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".