The neural and peptidergic control of gut contraction in <i>Locusta migratoria</i>: the effect of an FGLa/AST
Bibliographic record
Abstract
The regulation of insect gut physiology is complex and involves the interactions of a number of mechanisms, including the neural regulation of gut contraction by altering neural input and the modulation of gut contractions by neuropeptides directly affecting the muscle. The FGLa-type allatostatins (FGLa/ASTs) are known brain/gut peptides with numerous physiological roles, including modulation of gut contraction and neural input. To further investigate the pleiotropic roles of FGLa/AST peptides in Locusta migratoria, we have examined the role of a locust FGLa/AST (Scg-AST-6) in the gut. Proctolin and Scg-AST-6 have opposing effects on gut contraction, where proctolin dose-dependently increases gut muscle tension, while Scg-AST-6 inhibits both muscle tension and spontaneous and neurogenic contractions in a dose-dependent manner. Results from neurophysiological recordings indicate that there may be a central pattern generator (CPG) within the ventricular ganglia regulated by descending inhibition, and the addition of Scg-AST-6 dose-dependently modulates this ventricular ganglion CPG. This work provides a comprehensive picture of how FGLa/ASTs may modulate and coordinate each region of the locust gut, and shows that FGLa/ASTs have both central effects, on the ventricular ganglion CPG, and peripheral effects on the gut muscle. Overall, this study shows how FGLa/ASTs contribute to the complex regulation and fine tuning of gut contraction.
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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".