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Record W2807265262 · doi:10.21769/bioprotoc.2865

Hyaluronan Isolation from Mouse Mammary Gland

2018· article· en· W2807265262 on OpenAlexaff

Bibliographic record

VenueBIO-PROTOCOL · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsWestern UniversityVictoria HospitalLondon Health Sciences Centre
Fundersnot available
KeywordsHyaluronic acidGlycosaminoglycanIsolation (microbiology)Mammary glandExtracellularCellDistribution (mathematics)

Abstract

fetched live from OpenAlex

The glycosaminoglycan hyaluronan (HA) is a key component of the extracellular matrix. The molecular weight of HA is heterogeneous and can reach from several million to several hundred daltons. The effect of HA on cell behavior is size dependent; fragmented HA acts as a danger signal, stimulates cell migration and proliferation and is proinflammatory, native high molecular weight HA suppresses inflammation. Therefore, it is important to analyze HA size distribution when studying the role of HA in tissue homeostasis and pathology. This protocol describes isolation of HA from mouse mammary glands but can also be applied to other tissues. The quality of the isolated HA is sufficient to analyze size distribution by gel electrophoresis ( Calabro et al., 2000 ).

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)0.001
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.011

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 head0.018
GPT teacher head0.316
Teacher spread0.298 · 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
GenreMethods

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

Citations2
Published2018
Admission routes1
Has abstractyes

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