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Record W2587845015 · doi:10.1177/0003489417691296

Retention of Human-Induced Pluripotent Stem Cells (hiPS) With Injectable HA Hydrogels for Vocal Fold Engineering

2017· article· en· W2587845015 on OpenAlexaff
Mitsuyoshi Imaizumi, Nicole Y. K. Li‐Jessen, Yuka Sato, David T. Yang, Susan L. Thibeault

Bibliographic record

VenueAnnals of Otology Rhinology & Laryngology · 2017
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsMcGill University
FundersNational Institutes of Health
KeywordsHyaluronic acidVocal foldsScaffoldTissue engineeringHistologyInduced pluripotent stem cellRegeneration (biology)ImmunohistochemistrySelf-healing hydrogelsAnatomyMedicinePathologyBiomedical engineeringChemistryBiologyCell biologyEmbryonic stem cellLarynx

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.325
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2017
Admission routes1
Has abstractyes

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