Involvement of Insulin-Like Peptide in Long-Term Synaptic Plasticity and Long-Term Memory of the Pond Snail<i>Lymnaea stagnalis</i>
Bibliographic record
Abstract
The pond snail Lymnaea stagnalis is capable of learning taste aversion and consolidating this learning into long-term memory (LTM) that is called conditioned taste aversion (CTA). Previous studies showed that some molluscan insulin-related peptides (MIPs) were upregulated in snails exhibiting CTA. We thus hypothesized that MIPs play an important role in neurons underlying the CTA-LTM consolidation process. To examine this hypothesis, we first observed the distribution of MIP II, a major peptide of MIPs, and MIP receptor and determined the amounts of their mRNAs in the CNS. MIP II was only observed in the light green cells in the cerebral ganglia, but the MIP receptor was distributed throughout the entire CNS, including the buccal ganglia. Next, when we applied exogenous mammalian insulin, secretions from MIP-containing cells or partially purified MIPs, to the isolated CNS, we observed a long-term change in synaptic efficacy (i.e., enhancement) of the synaptic connection between the cerebral giant cell (a key interneuron for CTA) and the B1 motor neuron (a buccal motor neuron). This synaptic enhancement was blocked by application of an insulin receptor antibody to the isolated CNS. Finally, injection of the insulin receptor antibody into the snail before CTA training, while not blocking the acquisition of taste aversion learning, blocked the memory consolidation process; thus, LTM was not observed. These data suggest that MIPs trigger changes in synaptic connectivity that may be correlated with the consolidation of taste aversion learning into CTA-LTM in the Lymnaea CNS.
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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".