Different stressors uniquely affect the expression of endocannabinoid‐metabolizing enzymes in the central ring ganglia of <i>Lymnaea stagnalis</i>
Bibliographic record
Abstract
The endocannabinoid system (ECS) plays an important role in neuroprotection, neuroplasticity, energy balance, modulation of stress, and inflammatory responses, acting as a critical link between the brain and the body's peripheral regions, while also offering promising potential for novel therapeutic strategies. Unfortunately, in humans, pharmacological inhibitors of different ECS enzymes have led to mixed results in both preclinical and clinical studies. As the ECS has been highly conserved throughout the eukaryotic lineage, the use of invertebrate model organisms like the pond snail Lymnaea stagnalis may provide a flexible tool to unravel unexplored functions of the ECS at the cellular, synaptic, and behavioral levels. In this study, starting from the available genome and transcriptome of L. stagnalis, we first identified putative transcripts of all ECS enzymes containing an open reading frame. Each predicted protein possessed a high degree of sequence conservation to known orthologues of other invertebrate and vertebrate organisms. Sequences were confirmed by qualitative PCR and sequencing. Then, we investigated the transcriptional effects induced by different stress conditions (i.e., bacterial LPS injection, predator scent, food deprivation, and acute heat shock) on the expression levels of the enzymes of the ECS in Lymnaea's central ring ganglia. Our results suggest that in Lymnaea as in rodents, the ECS is involved in mediating inflammatory and anxiety-like responses, promoting energy balance, and responding to acute stressors. To our knowledge, this study offers the most comprehensive analysis so far of the ECS in an invertebrate model organism.
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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) | 0.001 | 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".