MétaCan
Menu
Back to cohort
Record W2066274808 · doi:10.1021/ac0488210

Entrapment of Highly Active Membrane-Bound Receptors in Macroporous Sol−Gel Derived Silica

2004· article· en· W2066274808 on OpenAlexafffund
Travis R. Besanger, E. Balasubramaniam, John D. Brennan

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Innovation Trust
KeywordsChemistryReceptorBioanalysisMembraneBiophysicsMesoporous silicaAcetylcholine receptorEntrapmentChromatographyLigand (biochemistry)Mesoporous materialBiochemistry

Abstract

fetched live from OpenAlex

The immobilization of membrane-associated proteins remains a challenging task. Herein, we report on the entrapment of two classes of membrane-bound receptors into sol-gel derived silica. Both nicotinic acetylcholine receptor (nAChR), a ligand-gated ion channel, and dopamine D(2Short) receptor (D2R), a G-protein coupled receptor, were entrapped into a series of sol-gel derived nanocomposite materials. In cases where the silica had a bimodal pore size distribution wherein both mesopores and macropores were present, the two receptors showed 40-80% of solution activity over periods of at least 1 month. Furthermore, the dissociation constants of entrapped nAChR and D2R for binding to known agonists and antagonists were very close to the values obtained for free receptors in solution. These results indicate that membrane-bound receptors entrapped into bimodal meso/macroporous silica should provide a useful platform for the development of bioanalytical devices such as bioaffinity columns or microarrays, which could aid in diagnosis and high-throughput drug screening.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.579

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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 designBench or experimental
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

Citations52
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicMolecular Junctions and NanostructuresFrench-language works237,207