Survivin gene therapy attenuates left ventricular systolic dysfunction in doxorubicin cardiomyopathy by reducing apoptosis and fibrosis
Bibliographic record
Abstract
AIMS: The aim of this study was to investigate anti-apoptotic gene therapy using ultrasound-mediated plasmid delivery of survivin, an inhibitor of apoptosis protein, to prevent apoptosis and to attenuate left ventricular (LV) systolic dysfunction in a model of heart failure induced by doxorubicin. METHODS AND RESULTS: Effect of survivin transduction was investigated in vitro in rat cardiomyoblasts. After survivin transduction, survivin protein was detected in cell culture supernate confirming secretion of extracellular survivin. Under doxorubicin stimulation, survivin-transduced cells had significantly reduced apoptosis; however, incubation with survivin-conditioned media also showed reduced apoptosis that was absent with null-conditioned media. Doxorubicin-induced cardiomyopathy was established in Fischer rats. Subsets of animals underwent ultrasound-mediated survivin gene delivery or empty vector gene delivery at Week 3. Control rats received doxorubicin alone. Animals were studied using PCR, immunohistochemistry, echocardiography, and invasive haemodynamic studies out to Week 6. By Week 6, LV % fractional shortening by echocardiography and systolic function by pressure-volume loops were greater in survivin treated when compared with control- and empty-treated animals. There was reduced apoptosis by TUNEL and caspase activity in survivin-treated animals compared with control and empty treated at Week 4, with reduced interstitial fibrosis at Week 6. CONCLUSION: Survivin gene therapy can attenuate the progression of LV systolic dysfunction in doxorubicin cardiomyopathy. This effect can be attributed to decreased myocyte apoptosis and prevention of maladaptive LV remodelling, by both direct myocyte transfection and potentially by paracrine mechanisms.
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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".