Cancer-selective therapy of the future: Apoptin and its mechanism of action
Bibliographic record
Abstract
Classical chemotherapy, that specifically targets rapidly proliferating cells, has been in existence for over eighty years and has proven to be fully successful in only a limited number of cancers. Thus, this review focuses on a novel, emerging approach for cancer therapy that uses alternative, and more unique features of cancer cells. This new approach facilitates the selective targeting of cancer, while sparing normal, non-transformed cells. Examples of molecules that kill cancer cells selectively are: apoptin, E4orf4, viral protein R (VpR), and Brevinin-2R. Below we focus on apoptin, a product of the third open reading frame (VP3) of the chicken anemia virus. Besides discussing apoptin's mechanism of action, we also provide concise insight into the biology of a chicken anemia virus infection. Since apoptin's cancer-selective toxicity depends on its nuclear localization, we broadly discuss mechanism(s) involved in its nuclear retention (both nuclear import and export). We also discuss recent findings on apoptin's molecular mechanism of action, with a focus on the role of Nur77 in apoptin's nucleo-cytoplasmic signaling. Finally, we compare the current findings on apoptin to the mechanism of cancer selective toxicity of E4orf4. In the 'summary' -section, besides highlighting important issues related to cancer-selective therapy, we also discuss concurrent approaches towards therapy personalization, particularly those related to the in vivo-, and real time cancer-therapy efficacy monitoring, using "lab-on-the-chip" and other emerging technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".