Fetal Bovine and Calf Serums Differ in Their Contents of Endocannabinoids, Unsaturated Fatty Acids, Monoacyl-Glycerols, <i>N</i> -Acyl-Ethanolamines, Oxylipins and Cytokines
Bibliographic record
Abstract
Abstract Cell culture relies heavily on serum supplementation, but serum composition makes it difficult to ensure reproducibility and consistency of experimental results. Consequently, research groups must test multiple batches to ensure the functionality and reproducibility of their models of their models. The commonly used serums are fetal bovine serums (FBS) and calf serums (CS), which have been recognized as crucial in modulating cellular processes. While having been utilized for decades, little information is known about their respective lipid mediator contents. This study explored the presence of several major bioactive lipids involved in the regulation of inflammation, metabolism, differentiation, immune response, neuroprotection, and vascular homeostasis. These included polyunsaturated fatty acids, monoacylglycerols (MAGs), N -acyl-ethanolamines (NAEs), and oxylipins. As compared to FBS, CS samples were enriched in most fatty acids except for arachidonic acid. The levels of the endocannabinoids 2-arachidonoyl-glycerol (2-AG) and N -arachidonoyl-ethanolamine (AEA) followed the same pattern as arachidonic acid. On the other hand, most lipoxygenase-derived mediators, including, leukotriene B 4 , showed higher abundance in CS. Accordingly, CS serum activated the random migration of human neutrophils to a much greater extent than FBS, an effect attenuated by the BLT 1 receptor antagonist CP 105,696. These findings highlight how bovine serum lipid composition is a major determinant modulating cellular responses and might thus impact experimental reproducibility. The data presented herein will help key insights beyond conventional cell culture optimization and might represent key features to consider when planning in cellulo experiments across numerous fields, also beyond immunology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".