Nicotinamide riboside promotes autolysosome clearance in preventing doxorubicin-induced cardiotoxicity
Bibliographic record
Abstract
Abstract Doxorubicin (DOX) is widely used as a first-line chemotherapeutic drug for various malignancies. However, DOX causes severe cardiotoxicity, which limits its clinical uses. Oxidative stress is one of major contributors to DOX-induced cardiotoxicity. While autophagic flux serves as an important defense mechanism against oxidative stress in cardiomyocytes, recent studies have demonstrated that DOX induces the blockage of autophagic flux, which contributes to DOX cardiotoxicity. The present study investigated whether nicotinamide riboside (NR), a precursor of nicotinamide adenine dinucleotide (NAD)+, prevents DOX cardiotoxicity by improving autophagic flux. We report that administration of NR elevated NAD+ levels, and reduced cardiac injury and myocardial dysfunction in DOX-injected mice. These protective effects of NR were recapitulated in cultured cardiomyocytes upon DOX treatment. Mechanistically, NR prevented the blockage of autophagic flux, accumulation of autolysosomes, and oxidative stress in DOX-treated cardiomyocytes, the effects of which were associated with restoration of lysosomal acidification. Furthermore, inhibition of lysosomal acidification or SIRT1 abrogated these protective effects of NR during DOX-induced cardiotoxicity. Collectively, our study shows that NR enhances autolysosome clearance via the NAD+/SIRT1 signaling, thereby preventing DOX-triggered cardiotoxicity.
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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 | 0.000 |
| 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".