Monitoring Cell Growth, Viability, and Apoptosis
Bibliographic record
Abstract
The accurate determination of cell growth and viability is pivotal to monitoring a bioprocess. Direct methods to determine the cell growth and/or viability in a bioprocess include microscopic counting, electronic particle counting, image analysis, in situ biomass monitoring, and dieletrophoretic cytometry. These methods work most simply when a fixed volume sample can be taken from a suspension culture. Manual microscopic counting is laborious but affords the advantage of allowing cell viability to be determined if a suitable dye is included. Electronic particle counting is a rapid total cell count method for replicate samples, but some data distortion may occur if the sample has significant cell debris or cell aggregates. Image analysis based on the use of digital camera images acquired through a microscope has advanced rapidly with the availability of several commercially available software packages replacing manual microscopic counting and viability determination. Biomass probes detect cells by their dielectric properties or their internal concentration of NADH and can be used as a continuous monitor of the progress of a culture. While the monitoring of cell growth and viability is an integral part of a bioprocess, the monitoring of apoptosis induction is also becoming more and more important in bioprocess control to increase volumetric productivity by extending bioprocess duration. Different fluorescent assays allow for the detection of apoptotic characteristics in a cell sample.Indirect methods of cell determination involve the chemical analysis of a culture component or a measure of metabolic activity. These methods are most useful when it is difficult to obtain intact cell samples. However, the relationship between these parameters and the cell number may not be linear through the phases of a cell culture. The determination of nucleic acid (DNA) or total protein can be used as an estimate of biomass, while the depletion of glucose from the media can be used as an estimate of cellular activity. The state of cellular viability may be measured by the release of an enzyme such as lactate dehydrogenase or more directly from the intracellular adenylate energy charge from cell lysates. Alternatively, radioactive techniques may be used for an accurate determination of cellular protein synthesis.
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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".