Retention of Human-Induced Pluripotent Stem Cells (hiPS) With Injectable HA Hydrogels for Vocal Fold Engineering
Bibliographic record
Abstract
OBJECTIVE: One prospective treatment option for vocal fold scarring is regeneration with an engineered scaffold containing induced pluripotent stem cells (iPS). In the present study, we investigated the feasibility of utilizing an injectable hyaluronic acid (HA) scaffold encapsulated with human-iPS cell (hiPS) for regeneration of vocal folds. METHODS: Thirty athymic nude rats underwent unilateral vocal fold injury. Contralateral vocal folds served as uninjured controls. Hyaluronic acid hydrogel scaffold, HA hydrogel scaffold containing hiPS, and HA hydrogel scaffold containing hiPS with epidermal growth factor (EGF) were injected in both vocal folds immediately after surgery. One and 2 weeks after injection, larynges were excised for histology, immunohistochemistry, and fluorescence in situ hybridization (FISH). RESULTS: Presence of HA hydrogel was confirmed in vocal folds 1 and 2 weeks post injection. The FISH analysis confirmed the presence and viability of hiPS in the injected vocal folds. Histological results demonstrated that vocal folds injected with HA hydrogel scaffold containing EGF demonstrated less fibrosis than those with HA hydrogel only. CONCLUSIONS: Human-iPS survived in injured rat vocal folds. The HA hydrogel with hiPS and EGF ameliorated the fibrotic response. Additional work is necessary to optimize hiPS differentiation and further confirm the safety of hiPS for clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".