Investigation of the effects of oxidative stress‐inducing factors on culturing and productivity of <i>Bordetella pertussis</i>
Bibliographic record
Abstract
Abstract The stress response of Bordetella pertussis during fermentation was assessed by means of fluorescence‐based techniques. During the manufacturing of vaccines, B. pertussis is subjected to stress during adaptation to a new environment and operating conditions in the bioreactor, which can have harmful consequences on growth and protein yield. In this study, stress was imposed by varying the percentage of dissolved oxygen (DO) and inoculum size, and by adding rotenone and hydrogen peroxide. In this study, fluorescence spectroscopy is used as a tool for measuring oxidative stress. High levels of DO during fed‐batch operation had no detrimental effect on growth, but the specific productivity of pertactin (PRN) decreased. Cultures that were started with an inoculum size that was 10 times smaller than the control resulted in significantly less PRN as compared to controls where reduction was more significant in flasks as compared to bioreactors. A comparison of filtered to heat‐sterilized media revealed that filtered media offered a protective effect against H 2 O 2 . Heat sterilization of the media might result in the destruction of components that offer protection against oxidative stress. Nonetheless, filter sterilization on its own would be insufficient for large‐scale manufacturing. It should be emphasized that the effects of these stressors while investigating for other microorganisms have not been studied for B. pertussis .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".