The Impact of Remote Ischemic Preconditioning on Inflammation Markers in Patients Undergoing Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Background: This study aimed to investigate if remote ischemic preconditioning reduces the inflammatory process on patients undergoing coronary artery bypass grafting (CABG). Methods: We conducted a case-control study involving 80 patients, half of whom underwent ischemic preconditioning for severe coronary artery disease (CAD) and subsequently underwent CABG. We assessed interleukin (IL)-1 and IL-6 levels using the enzyme-linked immunosorbent assay (ELISA) method, high-sensitivity troponin I (hsTnI) using chemiluminescent immunoassay (CLIA), and C-reactive protein (CRP) using the turbidimetric method at three key time points: before surgery (visit 1 or V1), immediately postoperatively (visit 2 or V2), and 1 week postoperatively (visit 3 or V3) in all subjects. Results: Ischemic preconditioned patients showed a significant decrease in proinflammatory markers (IL-1, IL-6) but not in CRP or hsTnI. Conclusions: This study demonstrated that remote ischemic preconditioning significantly reduced the levels of specific proinflammatory markers (IL-1 and IL-6), which may suggest general systemic protection. However, it did not demonstrate cardioprotection per se , as evidenced by the absence of a statistically significant decrease in hsTnI level. Cardiol Res. 2024;15(5):369-376 doi: https://doi.org/10.14740/cr1702
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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 | 0.001 |
| 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 | 0.000 |
| 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".