AKT pathway in neuroblastoma and its therapeutic implication
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".