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Record W4389336268 · doi:10.3390/vaccines11121819

Intensified Influenza Virus Production in Suspension HEK293SF Cell Cultures Operated in Fed-Batch or Perfusion with Continuous Harvest

2023· article· en· W4389336268 on OpenAlexafffund
Cristina A. T. Silva, Amine Kamen, Olivier Henry

Bibliographic record

VenueVaccines · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsMcGill UniversityPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health Research
KeywordsPerfusionBioreactorPulp and paper industryContinuous productionProcess engineeringEnvironmental scienceBiotechnologyFood scienceChemistryBiologyEnvironmental engineeringMedicineBotanyEngineering

Abstract

fetched live from OpenAlex

Major efforts in the intensification of cell culture-based viral vaccine manufacturing focus on the development of high-cell-density (HCD) processes, often operated in perfusion. While perfusion operations allow for higher viable cell densities and volumetric productivities, the high perfusion rates (PR) normally adopted—typically between 2 and 4 vessel volumes per day (VVD)—dramatically increase media consumption, resulting in a higher burden on the cell retention device and raising challenges for the handling and disposal of high volumes of media. In this study, we explore high inoculum fed-batch (HIFB) and low-PR perfusion operations to intensify a cell culture-based process for influenza virus production while minimizing media consumption. To reduce product retention time in the bioreactor, produced viral particles were continuously harvested using a tangential flow depth filtration (TFDF) system as a cell retention device and harvest unit. The feeding strategies developed—a hybrid fed-batch with continuous harvest and a low-PR perfusion—allowed for infections in the range of 8–10 × 106 cells/mL while maintaining cell-specific productivity comparable to the batch control, resulting in a global increase in the process productivity. Overall, our work demonstrates that feeding strategies that minimize media consumption are suitable for large-scale influenza vaccine production.

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 score0.063
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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