Collection of Serum- and Feeder-free Mouse Embryonic Stem Cell-conditioned Medium for a Cell-free Approach
Bibliographic record
Abstract
The capacity of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) to generate various cell types has opened new avenues in the field of regenerative medicine. However, despite their benefits, the tumorigenic potential of ESCs and iPSCs has long been a barrier for clinical applications. Interestingly, it has been shown that ESCs produce several soluble factors that can promote tissue regeneration and delay cellular aging, suggesting that ESCs and iPSCs can also be utilized as a cell-free intervention method. Therefore, the method for harvesting mouse embryonic stem cell (mESC)-conditioned medium (mESC-CM) with minimal contamination of serum components (fetal bovine serum, FBS) and feeder cells (mouse embryonic fibroblasts, MEFs) has been highly demanded. Here, the present study demonstrates an optimized method for the collection of mESC-CM under serum- and feeder-free conditions and for the characterization of mESC-CM using senescence-associated multiple readouts. This protocol will provide a method to collect pure mESC-specific secretory factors without serum and feeder contamination.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".