Cell cycle kinetics of expanding populations of neural stem and progenitor cells in vitro
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neural stem cells (NSCs) are undifferentiated, primitive cells with important potential applications including the replacement of neural tissue lost due to neurodegenerative diseases, including Parkinson's disease, as well as brain and spinal cord injuries, including stroke. We have developed methods to rapidly expand populations of mammalian stem and progenitor cells in neurosphere cultures. In the present study, flow cytometry was used in order to understand cell cycle activation and proliferation of neural stem and progenitor cells in suspension bioreactors. First, a protocol was developed to analyze the cell cycle kinetics of NSCs. As expected, neurosphere cells were found to cycle slowly, with a very small proportion of the cell population undergoing mitosis at any time. Large fractions (65-70%) of the cells were detected in G1, even in rapidly proliferating cultures, and significant fractions (20%) of the cells were in G0. Second, it was observed that different culturing methods influence both the proportion of neurosphere cells in each phase of the cell cycle and the fraction of actively proliferating cells. The results show that suspension culture does not significantly alter the cell cycle progression of neurosphere cells, while long-term culture (>60 days) results in significant changes in cell cycle kinetics. This suggests that when developing a process to produce neural stem cells for clinical applications, it is imperative to track the cell cycle kinetics, and that a short-term suspension bioreactor process can be used to successfully expand neurosphere cells.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it