Diaphragmatic dysfunction secondary to experimental lower torso ischaemia–reperfusion injury is attenuated by thermal preconditioning
Bibliographic record
Abstract
BACKGROUND: Preconditioning describes the process whereby tissue exposure to a subcritical stress confers protection from subsequent injuries. This study assessed diaphragmatic muscle function after lower torso ischaemia-reperfusion (IR) and the role of thermal preconditioning in attenuation of this injury. METHODS: Sprague-Dawley rats were randomized into three groups (24 per group): a control group, an IR group that had aortic cross-clamping for 1 h followed by reperfusion, and a third group that received thermal preconditioning 18 h before IR. Diaphragmatic function was assessed at 24 h, 48 h and 7 days. RESULTS: IR resulted in significant diaphragmatic twitch and tetanic dysfunction compared with control muscle. Thermal preconditioning significantly attenuated this injury (P < 0.05). Mean(s.e.m.) muscle twitch and tetanic forces in the IR group were 204.9(17.2) and 282.7(19.2) g respectively at 24 h. Corresponding twitch and tetanic forces in preconditioned muscle were 270.4(25.1) and 552.0(35.2) g. CONCLUSION: This study demonstrated that systemic IR injury produced a respiratory muscle mechanical dysfunction that was attenuated by thermal preconditioning, at 24 h, 48 h and 7 days. Preconditioning may have a role in clinical practice, particularly before elective surgery.
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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) | 0.003 | 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".