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Record W1978799932 · doi:10.1021/la050988g

Hydroxypropyl Guar−Borate Interactions with Tear Film Mucin and Lysozyme

2005· article· en· W1978799932 on OpenAlexaff
Chen Lu, L. Kris Kostanski, Howard A. Ketelson, David Meadows, Robert Pelton

Bibliographic record

VenueLangmuir · 2005
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLysozymeIsothermal titration calorimetryChemistryMucinPolyelectrolyteBoronMacromoleculeGuarCationic polymerizationGluteninBiophysicsPolymer chemistryBiochemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The interactions of hydroxypropyl guar (HPG) with boric acid, lysozyme, and mucin were characterized by rheology, light scattering, electrophoresis, and isothermal titration calorimetry to help understand how HPG interacts with tear film components. Borate binds to guar under pH, temperature, and ionic strength conditions representative of those found in the eye. The HPG-borate complexes behave as anionic polyelectrolytes and thus interact with cationic lysozyme, a major tear film protein, whereas HPG-borate does not appear to bind to mucin, an anionic glycoprotein. The interactions of HPG, borate, lysozyme, and mucin can be explained by two physical interactions: (1) pH-dependent binding of borate to carbohydrates and (2) the electrostatic attraction of oppositely charged macromolecules.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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.

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

Citations30
Published2005
Admission routes1
Has abstractyes

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