The Dendrite Arbor of Purkinje Cells Is Altered Following to Tail Regeneration in the Leopard Gecko
Bibliographic record
Abstract
Purkinje cells of the cerebellum have a complex arborized arrangement of dendrites and are among the most distinctive cell types of the nervous system. Although the neuromorphology of Purkinje cells has been well described for some mammals and teleost fish, for most vertebrates less is known. Here we used a modified Golgi-Cox method to investigate the neuromorphology of Purkinje cells from the lizard Eublepharis macularius, the leopard gecko. Using Sholl and Branch Structure Analyses, we sought to investigate whether the neuromorphology of gecko Purkinje cells was altered in response to tail loss and regeneration. Tail loss is an evolved mechanism commonly used by geckos to escape predation. Loss of the tail represents a significant and sudden change in body length and mass, which is only partially recovered as the tail is regenerated. We predicted that tail loss and regeneration would induce a quantifiable change in Purkinje cell dendrite arborization. Post hoc comparisons of Sholl analyses data showed that geckos with regenerated tails have significant changes in dendrite diameter and the number of dendrite intersections in regions corresponding to the position of parallel fiber synapses. We propose that the neuromorphological alterations observed in gecko Purkinje cells represent a compensatory response to tail regrowth, and perhaps a role in motor learning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".