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Mucins and Their Role in Shaping the Functions of Mucus Barriers

2018· review· en· W2895500801 on OpenAlexfundno aff
Caroline E. Wagner, Kelsey M. Wheeler, Katharina Ribbeck

Bibliographic record

VenueAnnual Review of Cell and Developmental Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
FundersDivision of Materials ResearchNational Institute of Biomedical Imaging and BioengineeringNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsMucinMucusBiologyGlycoproteinMicroscale chemistryCell biologyGlycosylationGlycanBiochemistryEcology

Abstract

fetched live from OpenAlex

We review what is currently understood about how the structure of the primary solid component of mucus, the glycoprotein mucin, gives rise to the mechanical and biochemical properties of mucus that are required for it to perform its diverse physiological roles. Macroscale processes such as lubrication require mucus of a certain stiffness and spinnability, which are set by structural features of the mucin network, including the identity and density of cross-links and the degree of glycosylation. At the microscale, these same features affect the mechanical environment experienced by small particles and play a crucial role in establishing an interaction-based filter. Finally, mucin glycans are critical for regulating microbial interactions, serving as receptor binding sites for adhesion, as nutrient sources, and as environmental signals. We conclude by discussing how these structural principles can be used in the design of synthetic mucin-mimetic materials and provide suggestions for directions of future work in this field.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.308
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations307
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Review of Cell and Developmental BiologySame topicGlycosylation and Glycoproteins ResearchFrench-language works237,207