Synapses on Sympathetic Neurons and Parasympathetic Neurons Differ in Their Vulnerability to Diabetes
Bibliographic record
Abstract
Synapses in autonomic ganglia represent the final output of various CNS structures that regulate the function of the periphery. Normally, these excitatory cholinergic-nicotinic synapses produce large suprathreshold EPSPs on sympathetic and parasympathetic neurons to convey signals from the CNS. However, in certain disease states, synaptic transmission in autonomic ganglia is depressed and the periphery becomes deregulated. For example, previous work demonstrated that hyperglycemia depresses EPSPs on sympathetic neurons and disrupts sympathetic reflexes by causing an ROS-dependent inactivation of the postsynaptic nAChRs. What is not clear, however, is whether some autonomic neurons are more vulnerable to hyperglycemia than others. One possibility is that sympathetic neurons may be more prone than cholinergic parasympathetic neurons to hyperglycemia-induced elevations in cytosolic ROS because sympathetic neurons contain several pro-oxidant molecules involved in noradrenaline metabolism. To test this hypothesis, we recorded synaptic transmission from different mouse sympathetic and parasympathetic ganglia, as well as from the adrenal medulla. In addition, we used cellular imaging to measure hyperglycemia-induced changes in cytosolic ROS and whole-cell recordings to measure the use-dependent rundown of ACh-evoked currents. Our results demonstrate that hyperglycemia depresses synaptic transmission on sympathetic neurons and adrenal chromaffin cells and elevates cytosolic ROS. Conversely, hyperglycemia has little effect on synaptic transmission at synapses on parasympathetic neurons. We conclude that sympathetic neurons and adrenal chromaffin cells are more vulnerable to diabetes than parasympathetic neurons, a finding that may have implications for both long-term diabetic autonomic neuropathies and insulin-induced hypoglycemia, a serious complication of diabetes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".