The role of retinoic acid in the formation and modulation of invertebrate central synapses
Bibliographic record
Abstract
Trophic factors can influence many aspects of nervous system function, such as neurite outgrowth, synapse formation, and synapse modulation. The vitamin A metabolite, retinoic acid, can exert trophic effects to promote neuronal survival and outgrowth in many species and is also known to modulate vertebrate hippocampal synapses. However, its role in synaptogenesis has not been well studied, and whether it can modulate existing invertebrate synapses is also not known. In this study, we first examined a potential trophic effect of retinoic acid on the formation of excitatory synapses, independently of its role in neurite outgrowth, using cultured neurons of the mollusc Lymnaea stagnalis We also investigated its role in modulating both chemical and electrical synapses between various Lymnaea neurons in cell culture. Although we found no evidence to suggest retinoic acid affected short-term synaptic plasticity in the form of post-tetanic potentiation, we did find a significant cell type-specific modulation of electrical synapses. Given the prevalence of electrical synapses in invertebrate nervous systems, these findings highlight the potential for retinoic acid to modulate network function in the central nervous system of at least some invertebrates. NEW & NOTEWORTHY: This study performed the first electrophysiological analysis of the ability of the vitamin A metabolite, retinoic acid, to exert trophic influences during synaptogenesis independently of its effects in supporting neurite outgrowth. It was also the first study to examine the ability of retinoic acid to modify both chemical and electrical synapses in any invertebrate, nonchordate species. We provide evidence that all-trans retinoic acid can modify invertebrate electrical synapses of central neurons in a cell-specific manner.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".