From Birth Till Death: Neurogenesis, Cell Cycle, and Neurodegeneration
Bibliographic record
Abstract
Neurogenesis in the embryo involves many signaling pathways and transcriptional programs and an elaborate orchestration of cell cycle exit in differentiating precursors. However, while the neurons differentiate into a plethora of different subtypes and different identities, they also presume a highly polar structure with a particular morphology of the cytoskeleton, thereby making it almost impossible for any differentiated cell to re-enter the cell cycle. It has been observed that dysregulated or forced cell cycle reentry is closely linked to neurodegeneration and apoptosis in neurons, most likely through changes in the neurocytoskeleton. However, proliferative cells still exist within the nervous system, and adult neural stem cells (NSCs) have been identified in the Central Nervous System (CNS) in the past decade, raising a great stir in the neuroscience community. NSCs present a new therapeutic potential, and much effort has since gone into understanding the molecular mechanisms driving differentiation of specific neuronal lineages, such as dopaminergic neurons, for use in regenerative medicine, either through transplanted NSCs or manipulation of existing ones. Nevertheless, differentiation and proliferation are two sides of the same coin, just like tumorigenesis and degeneration. Tumor formation may be regarded as a de-differentiation of tissues, where cell cycle mechanisms are reactivated in differentiated cell types. It is thus important to understand the molecular mechanisms underlying various brain tumors in this perspective. The recent Cancer Stem Cell (CSC) hypothesis also suggests the presence of Brain Tumor Initiating Cells (BTICs) within a tumor population, although the exact origin of these rare and mostly elusive BTICs are yet to be identified. This review attempts to investigate the correlation of neural stem cells/precursors, mature neurons, BTICs and brain tumors with respect to cell cycle regulation and the impact of cell cycle in neurodegeneration.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".