Transcranial Magnetic Stimulation Enhances the Therapeutic Effect of IGF-Trap in Intracerebral Glioma Models
Bibliographic record
Abstract
Background: Glioblastoma multiforme is an aggressive malignancy with a dismal 5-year survival rate of 5–10%. Current therapeutic options are limited, due in part to drug exclusion by the blood–brain barrier (BBB). We have previously shown that high-amplitude repetitive transcranial magnetic stimulation (rTMS) in rats allowed the delivery across the BBB of an IGF signaling inhibitor—IGF-Trap. The objective of this study was to assess the therapeutic effect of IGF-Trap when delivered in conjunction with rTMS on the intracerebral growth of glioma. Results: We found that systemic administration of IGF-Trap without rTMS had a minimal effect on the growth of orthotopically injected glioma cells in rats and mice, compared to control animals injected with vehicle only or treated with sham rTMS. In rats treated with a combination of rTMS and IGF-Trap, we observed a growth retardation of C6 tumors for up to 14 days post-tumor cell injection, although tumors eventually progressed. In mice, tumors were detectable in all control groups by 14–17 days post-injection of glioma GL261 cells and progressed rapidly thereafter. In mice treated with rTMS prior to IGF-Trap administration, tumor growth was inhibited or delayed, although the tumors also eventually progressed. Conclusion: The results showed that rTMS could increase the anti-tumor effect of IGF-Trap during the early phases of tumor growth. Further optimization of the rTMS protocol is required to improve survival outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".