AKT pathway in neuroblastoma and its therapeutic implication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neuroblastoma is a frequent pediatric tumor with a poor outcome in spite of aggressive treatment, even with autologous hematopoietic stem cell transplantation. The overall cure rate of 40% is unsatisfactory and new therapeutic strategies are urgently needed. AKT is a major mediator of survival signals that protect cells from apoptosis and regulate cell proliferation. The AKT signaling network is considered a key determinant of the biological aggressiveness of these tumors. In this article, the authors discuss the relation between activators of AKT in neuroblastoma, in particular, growth factors such as IGF-1, TRK, GDNF, VEGF and EGF, and their effects on tumoral proliferation, differentiation and apoptosis. Numerous other proteins interact with AKT in neuroblastoma. Several are relatively well characterized, such as PTEN and retinoic acid; others are new and potentially interesting, such as PKC and anaplastic lymphoma kinase. Specific inhibition of AKT has been studied, such as with LY249002, with significant effects on cell progression and apoptosis in tumoral cells. Moreover, a series of new drugs, such as geldanamycin and rapamycin, directly modify the expression of AKT in tumoral cells. Few specific inhibitors of AKT are available; less specific inhibitors are probably unsuitable therapeutic options in neuroblastoma. Drugs with a direct or indirect inhibitory effect on the AKT pathway, used alone or in combination with other drugs, seem to hold great promise as a new therapeutic modality in neuroblastoma.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it