Secretoneurin A regulates neurogenic and inflammatory transcriptional networks in goldfish (Carassius auratus) radial glia
Bibliographic record
Abstract
Radial glial cells (RGCs) are the most abundant macroglia in the teleost brain and have established roles in neurogenesis and neurosteroidogenesis; however, their transcriptome remains uncharacterized, which limits functional understanding of this important cell type. Using cultured goldfish RGCs, RNA sequencing and de novo transcriptome assembly were performed, generating the first reference transcriptome for fish RGCs with 17,620 unique genes identified. These data revealed that RGCs express a diverse repertoire of receptors and signaling molecules, suggesting that RGCs may respond to and synthesize an array of hormones, peptides, cytokines, and growth factors. Building upon neuroanatomical data and studies investigating direct neuronal regulation of RGC physiology, differential gene expression analysis was conducted to identify transcriptional networks that are responsive to the conserved secretogranin II-derived neuropeptide secretoneurin A (SNa). Pathway analysis of the transcriptome indicated that cellular processes related to the central nervous system (e.g., neurogenesis, synaptic plasticity, glial cell development) and immune functions (e.g., immune system activation, leukocyte function, macrophage response) were preferentially modulated by SNa. These data reveal an array of new functions that are proposed to be critical to neuronal-glial interactions through the mediator SNa.
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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) | 0.000 | 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".