Role of the autonomic nervous system in cardioprotection by remote preconditioning in isoflurane-anaesthetized dogs
Bibliographic record
Abstract
AIMS: Remote ischaemic preconditioning (rIPC) protects cardiac and non-cardiac tissues against ischaemic injury. Although there is increased demand to investigate its potential clinical applicability, fundamental mechanisms responsible for rIPC-mediated protection remain unresolved. We examined in isoflurane-anaesthetized dogs whether an intact cardiac nervous system was necessary to mediate rIPC protection against ischaemic injury. METHODS AND RESULTS: Dogs were randomly allocated to six groups: 1, control (CON, no-rIPC); 2, rIPC (4 × 5 min renal artery occlusion/reperfusion); 3, autonomic ganglionic blockade with hexamethonium (HEX, no-rIPC; 20 mg/kg iv); 4, HEX + rIPC; 5, cardiac decentralization by surgical ablation of extracardiac nerves (DCN, no-rIPC); and 6, DCN + rIPC. All dogs underwent 60 min coronary occlusion and 180 min reperfusion; cardiac haemodynamic parameters were monitored. Regional blood flow (microspheres) in the heart and kidneys was assessed. Necrotic tissue was visualized using triphenyltetrazolium staining and related to anatomic risk zone size (area at risk; P = NS between groups) and coronary collateral blood flow. Infarct size (% AAR) was 29 ± 5 (mean ± 1 SD) in CON and 15 ± 4 in rIPC dogs (P = 0.001 vs. CON); 24 ± 3 in HEX vs. 12 ± 2 in HEX + rIPC (P = 0.001 vs. HEX); and 20 ± 2 in DCN vs. 12 ± 4 in DCN + rIPC (P = 0.001 vs. DCN). In CON dogs, infarct size was inversely related to coronary collateral flow; this relation was shifted downwards in all groups pre-treated with rIPC. CONCLUSION: We report robust myocardial protection by rIPC against ischaemic injury in canines that was not abrogated by either pharmacological or surgical decentralization of cardiac nerves.
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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 | 0.001 |
| 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".