Cytoplasmic conductivity as a marker for bioprocess monitoring: Study of Chinese hamster ovary cells under nutrient deprivation and reintroduction
Bibliographic record
Abstract
Abstract The ability to monitor the status of cells during nutrient limitation is important for optimizing bioprocess growth conditions in batch and fed‐batch cultures. The activity level of Na + /K + ATPase pumps and cytoplasm ionic concentrations are directly influenced by the nutrient level, and thus, cytoplasm conductivity can be used as a markerless indicator of cell status. In this work, we monitored the change in cytoplasm conductivity of Chinese hamster ovary (CHO) cells during nutrient deprivation and reintroduction. Employing single cell dielectrophoresis, the change in cytoplasm conductivity was measured over a 48‐hr period. The conditions under which the cytoplasm conductivity would recover to a normal level after nutrient reintroduction was determined. In addition, numerical simulations of cell ion flux, for different levels of Na + /K + ATPase pump inhibition, were used to predict the minimum conductivity expected for nutrient‐deprived CHO cells. This predicted value is close to the minimum observed experimental cytoplasm conductivity for CHO cells that maintain the ability to restore the cytoplasm conductivity to the normal viable levels when nutrients are reintroduced. The recovery of starved cells was verified by reintroducing them to nutrient for 36 hr and measuring their proliferation using trypan blue exclusion assay. We conclude that cytoplasm conductivity can be used as a marker to indicate whether cells are in a recoverable state, such that the reintroduction of nutrients results in cells returning to a normal healthy state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".