Characterizing the physiological and behavioral roles of proctolin in<i>Drosophila melanogaster</i>
Bibliographic record
Abstract
The neuropeptide proctolin (RYLPT) plays important roles as both a neurohormone and a cotransmitter in arthropod neuromuscular systems. We used third-instar Drosophila larvae as a model system to differentiate synaptic effects of this peptide from its direct effects on muscle contractility and to determine whether proctolin can work in a cell-selective manner on muscle fibers. Proctolin did not appear to alter the amplitude of excitatory junctional potentials but did induce sustained muscle contractions in preparations where the CNS had been removed and no stimuli were applied to the remaining nerves. Proctolin-induced contractions were dose-dependent, were reduced by knocking down expression of the Drosophila proctolin receptor in muscle tissue, and were larger in some muscle cells than others (i.e., larger in fibers 4, 12, and 13 than in 6 and 7). Proctolin also increased the amplitude of nerve-evoked contractions in a dose-dependent manner, and the magnitude of this effect was also larger in some muscle cells than others (again, larger in fibers 4, 12, and 13 than in 6 and 7). Increasing the intraburst impulse frequency and number of impulses per burst increased the magnitude of proctolin's enhancement of nerve-evoked contractions and decreased the threshold and EC50 concentrations for proctolin to enhance nerve-evoked contractions. Reducing proctolin receptor expression decreased the velocity of larval crawling at higher temperatures, and thermal preference in these larvae. Our results suggest that proctolin acts directly on body-wall muscles to elicit slow, sustained contractions and to enhance nerve-evoked contractions, and that proctolin affects muscle fibers in a cell-selective manner.
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 | 0.001 |
| 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".