Real‐time monitoring of influenza virus production kinetics in HEK293 cell cultures
Bibliographic record
Abstract
There is an increased interest from the vaccine industry to use mammalian cell cultures for influenza vaccine manufacturing. Therefore, it became important to study the influenza infection mechanism, the viral-host interaction, and the replication kinetics from a bioprocessing stand point to maximize the influenza viral production yield in cell culture. In the present work, influenza replication kinetics was studied in HEK293 cells. Two infection conditions were evaluated, a low (0.01) and a high multiplicity of infection (1.0). Critical time points of the viral production cycle (infection, protein synthesis, viral assembly and budding, viral release, and host-cell death) were identified in small-scale cell cultures. Additionally, cell growth, viability, and viral titers were monitored in the viral production process. The infection state of the cultivated cell population was assessed by influenza immunolabeling throughout the culture period. Influenza virus production kinetics were also on-line monitored by dielectric spectroscopy and successfully correlated to real-time capacitance measures. Overall, this work provided insights into the mechanisms associated with the infection of human HEK293 cell line by the influenza virus and demonstrated, once again, the usefulness of multifrequency scanning permittivity for in-line monitoring and supervision of cell-based viral production processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".