Distinct receptors underlie glutamatergic signalling in inspiratory rhythm‐generating networks and motor output pathways in neonatal rat
Bibliographic record
Abstract
Despite the enormous diversity of glutamate (Glu) receptors and advances in understanding recombinant receptors, native Glu receptors underlying functionally identified inputs in active systems are poorly defined in comparison. In the present study we use UBP-302, which antagonizes GluR5 subunit-containing kainate (KA) receptors at < or = 10 microm, but other KA and AMPA receptors at > or = 100 microm, and rhythmically active in vitro preparations of neonatal rat to explore the contribution of non-NMDA receptor signalling in rhythm-generating and motor output compartments of the inspiratory network. At 10 microm, UBP-302 had no effect on inspiratory burst frequency or amplitude. At 100 microm, burst amplitude recorded from XII, C1 and C4 nerve roots was significantly reduced, but frequency was unaffected. The lack of a frequency effect was confirmed when local application of UBP-302 (100 microm) into the pre-Bötzinger complex (preBötC) did not affect frequency but substance P evoked a 2-fold increase. A UBP-302-sensitive (10 microm), ATPA-evoked frequency increase, however, established that preBötC networks are sensitive to GluR5 activation. Whole-cell recordings demonstrated that XII motoneurons also express functional GluR5-containing KA receptors that do not contribute to inspiratory drive, and confirmed the dose dependence of UBP-302 actions on KA and AMPA receptors. Our data provide the first evidence that the non-NMDA (most probably AMPA) receptors mediating glutamatergic transmission within preBötC inspiratory rhythm-generating networks are pharmacologically distinct from those transmitting drive to inspiratory motoneurons. This differential expression may ultimately be exploited pharmacologically to separately counteract depression of central respiratory rhythmogenesis or manipulate the drive to motoneurons controlling airway and pump musculature.
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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 | 0.001 |
| 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".