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Record W4365802753 · doi:10.1039/d3nr01109g

Efficient capture of recombinant SARS-CoV-2 receptor-binding domain (RBD) with citrate-coated magnetic iron oxide nanoparticles

2023· article· en· W4365802753 on OpenAlexafffund
David A. González‐Martínez, Gustavo González Ruíz, Cesar Escalante-Bermúdez, Judey Aymed García Artalejo, Tania Gómez Peña, José Alberto Gómez, Eduardo González‐Martínez, Yadira Cazañas Quintana, Thais Fundora Barrios, Tays Hernández, Roberto Carlos Varela Pérez, Dayli Díaz Goire, Diaselys Castro López, Ingrid Ruíz Ramirez, Carlos Díaz-Águila, Jose Moran‐Mirabal

Bibliographic record

VenueNanoscale · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsBrockhouse Institute for Materials ResearchMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic nanoparticlesRecombinant DNANanoparticleIron oxide nanoparticlesIron oxideChemistryMaterials scienceBiophysicsNanotechnologyChemical engineeringBiochemistryBiologyMetallurgy

Abstract

fetched live from OpenAlex

), which resulted in good capture yields (between 83 ± 5% and 94 ± 4%). The improvement of RBD purity after desorption was demonstrated by SDS-PAGE and RP-HPLC. Furthermore, the magnetic capture was scaled up 100 times, and the desorption was subjected to chromatographic purifications. The obtained products recognized anti-RBD antibodies and bound the ACE2 receptor, proving their functionality after the developed procedure.

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)0.001
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 score0.009
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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