Factors Influencing Percentage Yield of Side Population Isolated in Ovarian Cancer Cell LineSK-OV-3
Bibliographic record
Abstract
Isolation of side population (SP) cells has been recognized as a useful technique for the isolation and identification of hematopoietic stem cells or cancer stem cells (CSCs). Thus the yield and purity of isolated SP cells would have a profound influence on the research outcomes in these two important areas. Hoechst 33342 exclusion assay technique has been used for the identification of SP cells. However, diverse Hoechst staining protocols giving different SP yields even from the same tissue type or same cell line have been reported in different laboratories. In this study we systematically investigated the underlying factors influencing the SP yield using Hoechst dye staining and a robust platform of flow cytometric analysis of the human ovarian cancer cell line SK-OV-3. Our study revealed that SP yield was not only affected by the Hoechst 33342 concentration, staining cell density, staining cell viability, staining duration, staining medium, flow cytometric setting and SP gating strategy, but was also affected by the cell passage number in SK-OV-3. This is the first systematic study on the factors affecting SP yield in adherent cells that mimic many solid tumour tissues. Our results provide important technical guidelines to help ensure reproducible and comparable results in SP and CSCs study.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".