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Record W2021105398 · doi:10.1021/bc060302b

Binding of Streptavidin with Biotinylated Thermosensitive Nanospheres Based on Poly(<i>N,N</i>-diethylacrylamide-<i>co</i>-2-hydroxyethyl methacrylate)

2007· article· en· W2021105398 on OpenAlexaff
M. Colonne, Yong Chen, Kaili Wu, Stephan Freiberg, Suzanne Giasson, X. X. Zhu

Bibliographic record

VenueBioconjugate Chemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBiotinylationStreptavidinChemistry2-Hydroxyethyl MethacrylateMethacrylateConjugateCopolymerBovine serum albuminPolymerPolymer chemistryBiotinConjugated systemChemical engineeringBiophysicsChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Thermosensitive polymer nanospheres based on N,N-diethylacrylamide and 2-hydroxyethyl methacrylate (HEMA) have been prepared, characterized, and conjugated with biotin. The thermosensitivity of poly(N,N-diethylacrylamide) was enhanced by the incorporation of HEMA up to about 40 mol %. Atomic force microscopic images show that these particles can be closely packed even without the surface charges as in the latex particles. Biotinylation reduces the thermosensitivity of the copolymer nanospheres. The biotinylated hydrogel nanospheres showed a reduction in size upon binding with streptavidin, indicating the formation of a less hydrophilic conjugate. No aggregation of the biotinylated particles due to the cross-linking effect of streptavidin was observed. This size change could be reversed by the addition of free biotin to the system. The interaction is specific, and no such changes were observed when streptavidin was replaced by bovine serum albumin.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.006
GPT teacher head0.240
Teacher spread0.233 · 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 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

Citations28
Published2007
Admission routes1
Has abstractyes

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