Factors affecting the number of STRO-1+ stem cells derived from regenerating antler and pedicle cells of red and fallow deer
Bibliographic record
Abstract
Mesenchymal stem cells positive to surface antigen STRO-1 were isolated from regenerating antlers of red deer (Cervus elaphus) and fallow deer (Dama dama) using a magnetic-activated cell sorting (MACS) method. In this study we analysed factors potentially affecting the number of STRO-1+ cells in the cell cultures. With regard to the STRO-1 antigen, we evaluated data from 188 MACS separation procedures of cell cultures cultivated in Dulbecco’s Modified Eagle Medium and 10% fetal calf serum of four fallow deer males (130 procedures) and four red deer males (58 procedures). The analysed factors were the sampling site of the antler or the pedicle, cell passage and type of the cell culture (mixed or STRO-1 negative cell cultures). The percentage of obtained STRO-1+ cells varied greatly from 0.4 to 38.9% for fallow deer and from 1.8 to 16.5% for red deer. We have not found any significant influence of the sampling site. The passage and the type of culture were significant factors for both fallow and red deer cells. The highest numbers of STRO-1+ cells were obtained from the second passage from both fallow and red deer cell cultures (24.6 and 5.5%, respectively). Our experiment revealed that we can maximise the number of STRO-1+ cells in the cultures by manipulating the cultivation factors.
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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".