The IGF-1 receptor and its contributions to metastatic tumor growth—novel approaches to the inhibition of IGF-1R function
Bibliographic record
Abstract
Signals originating from the type 1 insulin-like growth factor receptor (IGF-1R) have pleiotropic effects on cell behavior. They regulate cell proliferation, survival, differentiation and transformation. IGF-1R also plays an essential role in the multistep process of cancer cell metastasis. Metastatic spread of tumor cells is the main reason for the high mortality rates associated with cancer. The development of strategies to inhibit this process promises important clinical benefits. Current attempts to block IGF-1R signaling has resulted in the reversion of the transformed phenotype, the induction of apoptosis, the sensitization to chemotherapeutic drugs and the reduction of the metastatic propensity of tumor cells. Since inhibition of IGF-1R has acceptable side effects on normal cells, the IGF-1R represents a favorable target for anticancer therapy. The well known structure and biochemical functions of the receptor suggest diverse strategies for interference. We will discuss strategies which have already been developed and suggest novel approaches based on peptide aptamers. These are peptides selected for specific binding to defined domains of the IGF-1R which offer subtle and specific possibilities to interfere with IGF-1R in the context of experimental tumor therapy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".