Production and Isolation of Axons from Sensory Neurons for Biochemical Analysis Using Porous Filters
Bibliographic record
Abstract
Neuronal axons use specific mechanisms to mediate extension, maintain integrity, and induce degeneration. An appropriate balance of these events is required to shape functional neuronal circuits. The protocol described here explains how to use cell culture inserts bearing a porous membrane (filter) to obtain large amounts of pure axonal preparations suitable for examination by conventional biochemical or immunocytochemical techniques. The functionality of these filter inserts will be demonstrated with models of developmental pruning and Wallerian degeneration, using explants of embryonic dorsal root ganglion. Axonal integrity and function is compromised in a wide variety of neurodegenerative pathologies. Indeed, it is now clear that axonal dysfunction appears much earlier in the course of the disease than neuronal soma loss in several neurodegenerative diseases, indicating that axonal-specific processes are primarily targeted in these disorders. By obtaining pure axonal samples for analysis by molecular and biochemical techniques, this technique has the potential to shed new light into mechanisms regulating the physiology and pathophysiology of axons. This in turn will have an impact in our understanding of the processes that drive degenerative diseases of the nervous system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".