The use of solid phase microextraction for metabolomic analysis of non-small cell lung carcinoma cell line (A549) after administration of combretastatin A4
Bibliographic record
Abstract
Use of solid phase microextraction (SPME) for cell culture metabolomic analysis allows for the attainment of more sophisticated data from in vitro cell cultures. Moreover, considering that SPME allows the implementation of multiple extractions from the same sample due to its non/low-depletive nature, time course studies using the same set of samples are thus facilitated via this method. Such an approach results in a reduction in the number of samples needed for analysis thus eliminates inter-batch variability related to biological variation occurring during cell culturing. The current work aims to demonstrate the capability of SPME for measurements of combretastatin A4 (CA4) effectiveness on non-small cell cancer cell line. A cultivation protocol was established in the 96-well plate, and a fiber format of SPME was selected for metabolite extraction. The extracellular metabolic pattern of cells was changed after administration of the tested drug. This suggests pharmacological activity of the administered compound towards the studied cell line model. Results support that the use of direct immersion SPME for analysis of cell cultures does not affect cells growth or contaminate sample. Consequently, SPME allows the attainment of accurate information regarding drug uptake, metabolism, and metabolomic changes in the studied cells induced by exposure to the drug simultaneously in a single experiment.
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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".