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Record W4416451597 · doi:10.1016/j.bbrep.2025.102368

Casein hydrolysates as scaffolds for gold nanocluster bioconjugation

2025· article· en· W4416451597 on OpenAlexafffund
Adalia Renée Wambolt, Victor Martinez-Macias, Brian Foo, Geniece L. Hallett-Tapley, Alexander C. Y. Foo

Bibliographic record

VenueBiochemistry and Biophysics Reports · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova Scotia
KeywordsBioconjugationNanoclustersCaseinScaffoldBiosensorMilk proteinProtein engineeringColloidal gold

Abstract

fetched live from OpenAlex

Protein-conjugated Gold Nanoclusters (AuNC) are an emerging material with extensive applications in medicine and biotechnology. Previous works describe the use of casein proteins from bovine milk as a facile and economical scaffold for the generation of AuNC bioconjugates. When attempting to replicate two such protocols, we observed significant hydrolysis of the casein scaffold. As such, the resulting materials are better described as AuNC-conjugated casein hydrolysates . While degradation of the casein scaffold did not appear to impact the biosensing functionalities attributed to them in previous works, the loss of protein structure could impair immune recognition and other biological processes which depend on the intact protein scaffold. These results represent an important clarification concerning the nature of the Casein-AuNC bioconjugates described in the literature, with implications for their utility in biomedical applications. • CaseIn is a promising scaffold for gold nanocluster bioconjugation. • Re-examination of previous protocols suggest degradation of casein scaffold upon bioconjugate formation. • Bioconjugate hydrolysates retain biosensing functionality. • Degradation of casein scaffold can disrupt antibody recognition, potentially enhancing biomedical applications.

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 score0.011
Threshold uncertainty score0.513

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 head0.006
GPT teacher head0.249
Teacher spread0.244 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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