Intracellular Fatty Acid Composition Affects Cell Yield, Energy Metabolism and Cell Damage in Agitated Cultures
Bibliographic record
Abstract
Continuous passage of cells in serum-free media requires the presence of micronutrients and growth factors to compensate for the lack of serum. Fatty acid supplementation is essential to ensure an adequate composition of the structural lipid components of the cell. We have shown that the unsaturated fatty acids, oleic (C18.1) and linoleic (C18.2) independently enhance cell yield and Mab productivity. The cellular content of the fatty acids gradually increased during continuous culture passage with no evidence of regulatory control. Most of the fatty acid accumulated in the polar lipid fraction and the unsaturated/ saturated fatty acid ratio of all cellular lipid fractions increased significantly. This caused a substantial decrease in the rate of glutamine metabolism and an increase in the rate of glucose metabolism. The changes in energy metabolismwere reversed when the cells were removed from fatty acid-supplemented medium. The most plausible explanation for this effect is an altered rate of transport of glutamine via the cell membrane. An observed change in the phospholipid composition of the membrane also caused a significant protective effect on the cells in agitated cultures. The life-span of fatty acid-loaded cells showed a x3 improvement compared to controls in cultures stirred at high rates of agitation.
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 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".