Evidence for Reactive Neurogenesis in the Forebrain of the Leopard Gecko (Eublepharis macularius)
Bibliographic record
Abstract
Neurogenesis is the ability to generate new neurons from resident progenitor populations. Although best understood as a normal physiological process, in several species of teleost fish and salamanders neurogenesis is also capable of replacing neurons lost or damaged due to injury – so called reactive neurogenesis. While reactive neurogenesis does not appear to resolve brain injuries in mammals, for reptiles less is known. Here, we investigated reactive neurogenesis in the lizard Eublepharis macularius, the leopard gecko. To initiate reactive neurogenesis, we administered geckos with a single dose of the neurotoxin 3‐acetylpyridine (3AP). Using the cell death markers Fluoro‐Jade and the TUNEL assay, we determined that neuronal loss occurs within 4 days following 3AP administration. Cell death is largely restricted to the medial and dorsomedial cortices, areas of the forebrain widely accepted to be the reptilian homologs of the mammalian hippocampus and neocortex, respectively. As evidenced by a decrease in the number of cells expressing the mature neuronal marker NeuN, cell death within these regions appears to selectively target neurons. Remarkably, by 30 days following 3AP administration the medial and dorsomedial cortices appear to be structurally restored with a pattern of NeuN expression that closely resembles the uninjured brain. These data provide the first evidence that the leopard gecko is capable of reactive neurogenesis, a phenomenon otherwise rare among amniotes (reptiles and mammals). Support or Funding Information Natural Sciences and Engineering Research Council (NSERC), Discovery Grant 400358)
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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 | 0.001 |
| 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".