Response by Dhingra et al to Letter Regarding Article, “Proteasomal Degradation of TRAF2 Mediates Mitochondrial Dysfunction in Doxorubicin-Cardiomyopathy”
Bibliographic record
Abstract
Anthracyclines such as Doxorubicin (Dox) are widely used for treating human cancers, yet a well-established but poorly understood phenomenon is their propensity for inducing heart failure.TRAF2, which serves as platform for NF-κB activation, was degraded in cardiac myocytes of cancer patients, mouse and human iPSCs derived cardiac myocytes following Dox treatment, resulting in widespread cell death and cardiac dysfunction.Conversely, gain of function of TRAF2 suppressed the cardiotoxic effects of Dox in vitro and in vivo 1 .Nevertheless, the net effect of TRAF2 on the anti-neoplastic properties of Dox was not investigated in this study and warrants future investigation in tumor bearing mouse models.Certainly, optimal therapies for Dox cardiotoxicity would either not effect or even enhance Dox anti-neoplastic efficacy.While some studies on TRAF2 suggest its oncogenic potential, in other contexts TRAF2 can act as a tumor suppressor 2 .Hence, the effect of TRAF2 may be tumor specific.With respect to in vitro and in vivo dosing of Dox, there are several challenges in recapitulating the exact dose and duration of Dox treatment that cancer patients experience in vivo (which varies with tumor type).In cell culture, it becomes even more challenging
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.037 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".