Study of Axonal Injury and Degeneration in<i>Drosophila</i>
Bibliographic record
Abstract
A fundamental feature of nervous systems is a highly specified synaptic connectivity between cells and the ability to adaptively change this connectivity through plasticity mechanisms. Plasticity mechanisms are highly relevant for responding to nervous system damage, and studies using nervous system injury paradigms in Drosophila (as well as other model organisms) have revealed conserved molecular pathways that are triggered by axon damage. Simple assays that introduce injuries to axons in either adult flies or larvae have proven to be particularly powerful for uncovering mechanisms of axonal degeneration and clearance. They have also been used to reveal requirements for regrowth of axons and dendrites, as well as signaling pathways that regulate cellular responses to nerve injury. Here we review commonly used and simple to carry out techniques that enable experimenters to study responses to axonal damage in either adult flies (following antennal transection) or larvae (following nerve crush to segmental nerves). Because axons and dendrites in the larval peripheral nervous system can be readily visualized through the translucent cuticle, another versatile method to probe injury responses is to focus high-energy laser light to a small and specific location in the animal. We therefore discuss a method for immobilizing intact larvae for imaging through the cuticle to carry out injury by pulse dye laser, which can be used to generate many different kinds of injuries and directed ablations within intact larvae. These techniques, combined with powerful genetic tools in Drosophila , make the fruit fly an excellent model system for studying the effects of injury and the mechanisms of axon degeneration, synapse plasticity, and immune response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".