MétaCan
Menu
Back to cohort
Record W4281879272 · doi:10.1186/s12929-022-00819-w

Bioengineering in salivary gland regeneration

2022· review· en· W4281879272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Biomedical Science · 2022
Typereview
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéUniversité Libre de BruxellesFonds De La Recherche Scientifique - FNRS
KeywordsRegeneration (biology)Salivary glandCell biologyBiologyAnatomyMedicinePathology

Abstract

fetched live from OpenAlex

Salivary gland (SG) dysfunction impairs the life quality of many patients, such as patients with radiation therapy for head and neck cancer and patients with Sjögren's syndrome. Multiple SG engineering strategies have been considered for SG regeneration, repair, or whole organ replacement. An in-depth understanding of the development and differentiation of epithelial stem and progenitor cells niche during SG branching morphogenesis and signaling pathways involved in cell-cell communication constitute a prerequisite to the development of suitable bioengineering solutions. This review summarizes the essential bioengineering features to be considered to fabricate an engineered functional SG model using various cell types, biomaterials, active agents, and matrix fabrication methods. Furthermore, recent innovative and promising approaches to engineering SG models are described. Finally, this review discusses the different challenges and future perspectives in SG bioengineering.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.056
GPT teacher head0.349
Teacher spread0.292 · 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