Inhibition of voltage‐gated Ca <sup>2+</sup> channels by sst4 stimulation is mediated by Gβγ and PKC activation
Bibliographic record
Abstract
Healthcare System, Los Angeles, CA Stimulation of somatostatin subtype‐4 receptors (sst 4 ) in isolated retinal ganglion cells (RGCs) inhibits Ca 2+ channel current (I Ca ). Reducing intracellular Ca 2+ is known to be neuroprotective; thus, stimulating sst 4 may prevent RGC loss in trauma and disease. The aim of this study was to determine the signaling pathways involved in sst 4 stimulation leading to suppression of I Ca in RGCs. Isolated RGCs were prepared using a modified Thy‐1 immunopanning procedure. Electrophysiology experiments were performed using whole‐cell patch clamp to isolate I Ca . L‐803,087 (sst 4 agonist, L‐803) inhibited I Ca by 40.4% and reduced calcium conductance (2.1 vs. 1.4 nS; p<0.05). Pretreatment of cells with pertussis toxin did not prevent the action of L‐803 (34.7%). Pre‐pulse facilitation did not reverse the inhibitory effects of L‐803 on I Ca (11.4 vs. 9.7 %). However, pharmacologic inhibition of Gβγ prevented I Ca suppression by L‐803 (23.0%, p<0.05). Inhibition of PKC (GF109203X; GFX) showed a concentration‐dependent effect in preventing the action of L‐803 on I Ca (1 μM GFX, 34.3%; 5 μM GFX, 18.4%, p<0.05). This suggests that sst 4 stimulation modulates RGC Ca 2+ channels via Gβγ and PKC activation. Thus, modulating these signaling pathways may provide unique therapeutic targets to reduce intracellular Ca 2+ levels in RGCs. Support from CIHR‐NSHRF RPP, NIH.
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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) | 0.001 | 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) | 0.006 | 0.002 |
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".