Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons
Bibliographic record
Abstract
Cerebellar granule neurons (CGNs) are a commonly used neuronal model, forming an abundant homogeneous population in the cerebellum. In light of their post-natal development, abundance, and accessibility, CGNs are an ideal model to study neuronal processes, including neuronal development, neuronal migration, and physiological neuronal activity stimulation. In addition, CGN cultures provide an excellent model for studying different modes of cell death including excitotoxicity and apoptosis. Within a week in culture, CGNs express N-methyl-D-aspartate (NMDA) receptors, a specific ionotropic glutamate receptor with many critical functions in neuronal health and disease. The addition of low concentrations of NMDA in conjunction with membrane depolarization to rodent primary CGN cultures has been used to model physiological neuronal activity stimulation while the addition of high concentrations of NMDA can be employed to model excitotoxic neuronal injury. Here, a method of isolation and culturing of CGNs from 6 day old pups as well as genetic manipulation of CGNs by adenoviruses and lentiviruses are described. We also present optimized protocols on how to stimulate NMDA-induced excitotoxicity, low-potassium-induced apoptosis, oxidative stress and DNA damage following transduction of these neurons.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".