Induction of LTM following an Insulin Injection
Bibliographic record
Abstract
learns conditioned taste aversion (CTA) and consolidates it into long-term memory (LTM). One-day food-deprived snails (day 1 snails) show the best CTA learning and memory, whereas more severely food-deprived snails (5 d) do not express good memory. However, previous studies showed that CTA-LTM was indeed formed in 5-d food-deprived snails (day 5 snails), but its recall was prevented by the effects of food deprivation. CTA-LTM recall in day 5 snails was expressed following 7 d of feeding and then 1 d of food deprivation (day 13 snails). In the present study, we thus hypothesized that memory recall occurs because day 13 snails are in an optimal internal state. One day of food deprivation before the memory test in day 13 snails increased the mRNA level of molluscan insulin-related peptide (MIP) in the CNS. Thus, we further hypothesized that an injection of insulin into day 5 snails following seven additional days with access to food (day 12 snails) activates CTA neurons and mimics the food deprivation state before the memory test in day 13 snails. Day 12 snails injected with insulin could recall the memory. In addition, the simultaneous injection of an anti-insulin receptor antibody and insulin into day 12 snails did not allow memory recall. Insulin injection also decreased the hemolymph glucose concentration. Together, the results suggest that an optimal internal state (i.e., a spike in insulin release and specific glucose levels) are necessary for LTM recall following CTA training in snails.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".