Controlled Semi-Automated Laser-Induced Injuries for Studying Spinal Cord Regeneration in Zebrafish Larvae
Bibliographic record
Abstract
Zebrafish larvae possess a fully functional central nervous system (CNS) with a high regenerative capacity only a few days after fertilization. This makes this animal model very useful for studying spinal cord injury and regeneration. The standard protocol for inducing such lesions is to transect the dorsal part of the trunk manually. However, this technique requires extensive training and damages additional tissues. A protocol was developed for laser-induced lesions to circumvent these limitations, allowing for high reproducibility and completeness of spinal cord transection over many animals and between different sessions, even for an untrained operator. Furthermore, tissue damage is mainly limited to the spinal cord itself, reducing confounding effects from injuring different tissues, e.g., skin, muscle, and CNS. Moreover, hemi-lesions of the spinal cord are possible. Improved preservation of tissue integrity after laser injury facilitates further dissections needed for additional analyses, such as electrophysiology. Hence, this method offers precise control of the injury extent that is unachievable manually. This allows for new experimental paradigms in this powerful model in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".