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Record W1987495653 · doi:10.1021/bc050052j

Methods for the Preparation of Protein−Oligonucleotide−Lipid Constructs

2006· article· en· W1987495653 on OpenAlexaff
Jennifer Takasaki, Sam G. Raney, Ghania Chikh, Laura Sekirov, Irina Brodsky, Ying K. Tam, Steven M. Ansell

Bibliographic record

VenueBioconjugate Chemistry · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsInimex Pharmaceuticals (Canada)
Fundersnot available
KeywordsChemistryOvalbuminMicelleAntigenOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

A mixture of ionizable cationic lipids, steric barrier lipids, and colipids is used to encapsulate oligonucleotide DNA in lipidic particles called SALP. This material is under development as an adjuvant for vaccines. Previously we have shown that coupling the antigen directly to the surface of SALP can lead to enhanced immunological responses in vivo. Two different methods for preparing ovalbumin-SALP were assessed in this work. Originally the conjugates were prepared by treating SALP containing a maleimide-derivatized lipid with thiolated ovalbumin, a method we refer to as active coupling. This reaction was found to be difficult to control and generally resulted in low coupling efficiencies. The issues relating to this approach were characterized. We have recently developed alternative techniques based on first coupling ovalbumin to a micelle and then incubating the resultant product with SALP, methods we refer to as passive coupling. We have shown that this method allows accurate control of the levels of protein associated SALP and does not suffer from surface saturation effects seen with the active coupling method that places maximum limits on the amount of protein that can be coupled to the SALP surface. The products from the passive coupling protocol are shown to have activity comparable to those derived from the active coupling protocol in investigations of in vivo immune responses.

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 score0.047
Threshold uncertainty score0.446

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 head0.012
GPT teacher head0.312
Teacher spread0.301 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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