The circular RNA circNlgnmediates doxorubicin-inducedcardiac remodeling and fibrosis
Bibliographic record
Abstract
Doxorubicin is a chemotherapeutic medication commonly used to treat many types of cancers, but it has side effects including vomiting, rash, hair loss, and bone marrow suppression. The most dangerous side effects are cardiomyopathy, cardiofibrosis, and heart failure, as doxorubicin generates cytotoxicity and stops DNA replication. There is no treatment to block these side effects. We have developed a transgenic mouse line overexpressing the circular RNA circNlgn and shown that circNlgn is a mediator of doxorubicin-induced cardiofibrosis. Increased expression of circNlgn decreased cardiac function and induced cardiofibrosis by upregulating Gadd45b, Sema4C, and RAD50 and activating p38 and pJNK in circNlgn transgenic heart. Silencing circNlgn decreased the effects of doxorubicin on cardiac cell activities and prevented doxorubicin-induced expression of fibrosis-associated molecules. The protein (Nlgn173) translated by circNlgn could bind and activate H2AX, producing γH2AX, resulting in upregulation of IL-1b, IL-2Rb, IL-6, EGR1, and EGR3. We showed that silencing these molecules in the signaling pathway prevented doxorubicin-induced cardiomyocyte apoptosis, increased cardiomyocyte viability, decreased cardiac fibroblast proliferation, and inhibited collagen production. This mechanism may hold therapeutic implications for mitigating the side effects of doxorubicin therapy in cancer patients.
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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".