A positive feedback loop between sensory and octopaminergic neurons underlies nociceptive plasticity in <i>Drosophila</i> larvae
Bibliographic record
Abstract
Abstract Adaptive modulation of nociceptive behaviour based on prior experience is essential for responding effectively to environmental threats. In Drosophila larvae, nociceptive escape behaviours are robust and stereotyped, yet emerging evidence suggests this can be modulated by experience and internal state. Here, we demonstrate that repeated activation of nociceptive sensory neurons enhances both the likelihood and intensity of nocifensive rolling, reflecting a form of behavioural sensitization. This heightened responsiveness is accompanied by a sustained increase in activity within nociceptive sensory neurons, suggesting that plasticity arises, at least in part, within the sensory compartment. We identified the neuromodulator octopamine as a critical regulator of the sensitization: signalling through the octopamine receptor OAMB is required to sustain elevated nociceptive gain, and feedback from one of octopaminergic neurons class, the ventral unpaired median (VUM) neurons, amplifies sensory neuron output. Together, these findings reveal an experience-dependent positive feedback loop in the nociceptive system, where neuromodulatory circuits tune behavioural output.
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 | 0.000 |
| 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".