A Flow Perfusion Bioreactor System for Vocal Fold Tissue Engineering Applications
Bibliographic record
Abstract
The human vocal folds (VFs) undergo complex biomechanical stimulation during phonation. The aim of the present study was to develop and validate a phono-mimetic VF flow perfusion bioreactor, which mimics the mechanical microenvironment of the human VFs in vitro. The bioreactor uses airflow-induced self-oscillations, which have been shown to produce mechanical loading and contact forces that are representative of human phonation. The bioreactor consisted of two synthetic VF replicas within a silicone body. A cell-scaffold mixture (CSM) consisting of human VF fibroblasts, hyaluronic acid, gelatin, and a polyethylene glycol cross-linker was injected into cavities within the replicas. Cell culture medium (CCM) was perfused through the scaffold by using a customized secondary flow loop. After the injection, the bioreactor was operated with no stimulation over a 3-day period to allow for cell adaptation. Phonation was subsequently induced by using a variable speed centrifugal blower for 2 h each day over a period of 4 days. A similar bioreactor without biomechanical stimulation was used as the nonphonatory control. The CSM was harvested from both VF replicas 7 days after the injection. The results confirmed that the phono-mimetic bioreactor supports cell viability and extracellular matrix proteins synthesis, as expected. Many scaffold materials were found to degrade because of challenges from phonation-induced biomechanical stimulation as well as due to biochemical reactions with the CCM. The bioreactor concept enables future investigations of the effects of different phonatory characteristics, that is, voice regimes, on the behavior of the human VF cells. It will also help study the long-term functional outcomes of the VF-specific biomaterials before animal and clinical studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".