Distribution and Function of GABA<sub>B</sub>Receptors in Spider Peripheral Mechanosensilla
Bibliographic record
Abstract
The mechanosensilla in spider exoskeleton are innervated by bipolar neurons with their cell bodies close to the cuticle and dendrites attached to it. Numerous efferent fibers synapse with peripheral parts of the mechanosensory neurons, with glial cells surrounding the neurons, and with each other. Most of these efferent fibers are immunoreactive to gamma-aminobutyric acid (GABA), and the sensory neurons respond to agonists of ionotropic GABA receptors with a rapid and complete inhibition. In contrast, little is known about metabotropic GABAB receptors that may mediate long-term effects. We investigated the distribution of GABAB receptors on spider leg mechanosensilla using specific antibodies against 2 proteins needed to form functional receptors and an antibody that labels the synaptic vesicles on presynaptic sites. Both anti-GABAB receptor antibodies labeled the distal parts of the sensory cell bodies and dendrites but anti-GABABR1 immunoreactivity was also found in the axons and proximal parts of the cell bodies and some glial cells. The fine efferent fibers that branch on top of the sensory neurons did not show GABAB receptor immunoreactivity but were densely labeled with anti-synapsin and indicated synaptic vesicles on presynaptic locations to the GABAB receptors. Intracellular recordings from sensory neurons innervating the slit sensilla of the spider legs revealed that application of GABAB receptor agonists attenuated voltage-activated Ca2+ current and enhanced voltage-activated outward K+ current, providing 2 possible mechanisms for controlling the neurons' excitability. These findings support the hypothesis that GABAB receptors are present in the spider mechanosensilla where their activation may modulate information transmission.
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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".