Effect of Low-Level Tragus Stimulation on Cardiac Metabolism in Heart Failure with Preserved Ejection Fraction: A Transcriptomics-Based Analysis
Bibliographic record
Abstract
Abnormal cardiac metabolism precedes and contributes to structural changes in heart failure. Low-level tragus stimulation (LLTS) can attenuate structural remodeling in heart failure with preserved ejection fraction (HFpEF). The role of LLTS on cardiac metabolism is not known. Dahl salt-sensitive rats of 7 weeks of age were randomized into three groups: low salt (0.3% NaCl) diet (control group; n = 6), high salt diet (8% NaCl) with either LLTS (active group; n = 8), or sham stimulation (sham group; n = 5). Both active and sham groups received the high salt diet for 10 weeks with active LLTS or sham stimulation (20 Hz, 2 mA, 0.2 ms) for 30 min daily for the last 4 weeks. At the endpoint, left ventricular tissue was used for RNA sequencing and transcriptomic analysis. The Ingenuity Pathway Analysis tool (IPA) was used to identify canonical metabolic pathways and upstream regulators. Principal component analysis demonstrated overlapping expression of important metabolic genes between the LLTS, and control groups compared to the sham group. Canonical metabolic pathway analysis showed downregulation of the oxidative phosphorylation (Z-score: −4.707, control vs. sham) in HFpEF and LLTS improved the oxidative phosphorylation (Z-score = −2.309, active vs. sham). HFpEF was associated with the abnormalities of metabolic upstream regulators, including PPARGC1α, insulin receptor signaling, PPARα, PPARδ, PPARGC1β, the fatty acid transporter SLC27A2, and lysine-specific demethylase 5A (KDM5A). LLTS attenuated abnormal insulin receptor and KDM5A signaling. HFpEF is associated with abnormal cardiac metabolism. LLTS, by modulating the functioning of crucial upstream regulators, improves cardiac metabolism and mitochondrial oxidative phosphorylation.
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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) | 0.001 | 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".