The History and Design of Assays for the Identification and Characterization of Neural Stem Cells
Bibliographic record
Abstract
For the better part of the last century it was firmly believed that the adult mammalian central nervous system did not have the capability to generate new cells after injury or disease. This was commonly referred to as the “no new neuron” dogma and was originally credited to the highly influential neurobiologist Santiago Ramon Cajal. Based on the evidence he had at that time, which was limited by the available methods and technology, he concluded that new neurons were not generated once CNS development had ended in adult mammals. This dogma was challenged in the early 1990s based on the introduction of new methodologies to label dividing cells in the CNS, the firm establishment of new cell genesis in non-mammalian species and the discovery of a bona fide stem cell in the mature mammalian brain. Isolation of a cell from the brain of adult mice that exhibited extensive proliferation and multi-lineage differentiation potential established the existence of a neural stem cell and raised the possibility that the mature mammalian CNS have the ability to repair itself. The method is referred to as the Neurosphere Assay (NSA) and has become a standard methodology for isolating stem cells from the developing and mature central nervous system of mammals. While this assay has been well accepted, and is widely used, it is not without its shortcomings and new assays have been developed to address these. Within this article we will highlight the pros and cons of the NSA and detail the methodologies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".