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Record W3207065738 · doi:10.3390/vaccines9101154

A Four-Step Purification Process for Gag VLPs: From Culture Supernatant to High-Purity Lyophilized Particles

2021· article· en· W3207065738 on OpenAlexaff
Irene González‐Domínguez, Elianet Lorenzo, Alice Bernier, Laura Cervera, Francesc Gòdia, Amine Kamen

Bibliographic record

VenueVaccines · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsMcGill University
Fundersnot available
KeywordsDownstream processingChromatographyChemistryVirus-like particleCell cultureVesicleFiltration (mathematics)Size-exclusion chromatographyMembraneRecombinant DNABiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

Gag-based virus-like particles (VLPs) have high potential as scaffolds for the development of chimeric vaccines and delivery strategies. The production of purified preparations that can be preserved independently from cold chains is highly desirable to facilitate distribution and access worldwide. In this work, a nimble purification has been developed, facilitating the production of Gag VLPs. Suspension-adapted HEK 293 cells cultured in chemically defined cell culture media were used to produce the VLPs. A four-step downstream process (DSP) consisting of membrane filtration, ion-exchange chromatography, polishing, and lyophilization was developed. The purification of VLPs from other contaminants such as host cell proteins (HCP), double-stranded DNA, or extracellular vesicles (EVs) was confirmed after their DSP. A concentration of 2.2 ± 0.8 × 109 VLPs/mL in the lyophilized samples was obtained after its storage at room temperature for two months. Morphology and structural integrity of purified VLPs was assessed by cryo-TEM and NTA. Likewise, the purification methodologies proposed here could be easily scaled up and applied to purify similar enveloped viruses and vesicles.

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 score0.034
Threshold uncertainty score0.515

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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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

Citations25
Published2021
Admission routes1
Has abstractyes

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