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Record W3108814519 · doi:10.3390/mi11121066

Tissue Engineering of Oral Mucosa and Salivary Gland: Disease Modeling and Clinical Applications

2020· review· en· W3108814519 on OpenAlexaff
Akram Abdo Almansoori, Bongju Kim, Jong-Ho Lee, Simon D. Tran

Bibliographic record

VenueMicromachines · 2020
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsDecellularizationOral mucosaTissue engineeringSalivary glandOrganoidOrgan culturePathologyExtracellular matrix3D cell cultureCell cultureOrgan-on-a-chipIn vitroMedicineBiologyCell biologyBiomedical engineeringNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Oral mucosa and salivary gland are composed of complex and dynamic networks of extracellular matrix, multiple cell types, vasculature, and various biochemical agents. Two-dimensional (2D) cell culture is commonly used in testing new drugs and experimental therapies. However, 2D cell culture cannot fully replicate the architecture, physiological, and pathological microenvironment of living human oral mucosa and salivary glands. Recent microengineering techniques offer state of the science cell culture models that can recapitulate human organ structures and functions. This narrative review describes emerging in vitro models of oral and salivary gland tissue such as 3D cell culture models, spheroid and organoid models, tissue-on-a-chip, and functional decellularized scaffolds. Clinical applications of these models are also discussed in this review.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.372
Teacher spread0.330 · 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 designOther design
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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