The Toll-like Receptor 9 Ligand CPG-C Attenuates Acute Inflammatory Cardiac Dysfunction
Bibliographic record
Abstract
Stimulation of toll-like receptor 9 (TLR9) by CpG-C containing oligonucleotides attenuates ischemic injury in the brain and liver. In this study, we investigate whether any of the three classes of CpG (A, B, or C) mitigate ischemia-induced cardiac dysfunction. We measured left ventricular ejection fraction (LVEF) in C57BL/6 mice using transthoracic echocardiography. Using LPS as an inflammatory stimulus, CpG-C was uniquely able to prevent cardiac dysfunction; its activity was confirmed through nuclear factor κB transcriptional activity assay in HL-1 cardiomyocytes. We went on to investigate CpG-C's efficacy and mechanism in the treatment of ischemia-reperfusion. Compared with baseline, no class of CpG significantly altered LVEF at 6 or 24 h; 40 mg/kg LPS induced a rapid, profound suppression of LVEF compared with baseline (26% ± 1.4% vs. 65% ± 1.4%), whereas pretreatment with CpG demonstrated that of the three classes, only CpG-C prevented the LPS -induced decrease in LVEF (51% ± 5.8%). In separate mice, 1-h ischemia followed by reperfusion of the left anterior descending artery resulted in a 7-day suppression of the LVEF (66% ± 5.2% at baseline; 46% ± 4.7% at day 1, and 46% ± 4.0% at day 7), whereas mice either pretreated with or begun on an infusion of CpG-C during the ischemia had no significant decline in LVEF. Gene expression microarray of CpG-C-stimulated cells revealed upregulation of the nuclear factor κB pathway inhibitors TNFAIP3, NFKBIA, TRIM30, and TNIP1. These may play a role in attenuation of cardiac inflammation. The TLR9 ligand CpG-C attenuates the acute inflammatory cardiac dysfunction induced by both LPS and ischemia-reperfusion of the left anterior descending artery.
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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) | 0.001 | 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".