Trends in Injectable Biomaterials for Vocal Fold Regeneration and Long-Term Augmentation
Bibliographic record
Abstract
Human vocal folds (VFs), a pair of small, soft tissues in the larynx, have a layered mucosal structure with unique mechanical strength to support high-level tissue deformation by phonation. Severe pathological changes to VF have causes including surgery, trauma, age-related atrophy, and radiation, and lead to partial or complete communication loss and difficulty in breathing and swallowing. VF glottal insufficiency requires injectable VF biomaterials such as hyaluronan, calcium hydroxyapatite, and autologous fat to augment VF functions. Although these biomaterials provide an effective short-term solution, significant variations in patient response and requirements of repeat reinjection remain notable challenges in clinical practice. Tissue engineering strategies have been actively explored in the search of an injectable biomaterial that possesses the capacity to match native tissue’s material properties while promoting permanent tissue regeneration. This review aims to assess the current status of biomaterial development in VF tissue engineering. The focus will be on examining state-of-the-art techniques including modification with bioactive molecules, cell encapsulation, composite materials, and in situ crosslinking with click chemistry. We will discuss potential opportunities that can further leverage these engineering techniques in the advancement of VF injectable biomaterials. Impact StatementInjectable vocal fold (VF) biomaterials augment tissue function through minimally invasive procedures, yet there remains a need for long-term VF reparation. This article reviews cutting-edge research in VF biomaterial development to propose safe and effective tissue engineering strategies for improving regenerative outcomes. Special focus is paid to methods to enhance bioactivity and achieve tissue-mimicking mechanical properties, longer in situ stability, and inherent biomaterial bioactivity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".