Neural Stem Cell Targeting of Glioma Is Dependent on Phosphoinositide 3-Kinase Signaling
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
The utility of neural stem cells (NSCs) has extended beyond regenerative medicine to targeted gene delivery, as NSCs possess an inherent tropism to solid tumors, including invasive gliomas. However, for optimal clinical implementation, an understanding of the molecular events that regulate NSC tumor tropism is needed to ensure their safety and to maximize therapeutic efficacy. We show that human NSC lines responded to multiple tumor-derived growth factors and that hepatocyte growth factor (HGF) induced the strongest chemotactic response. Gliomatropism was critically dependent on c-Met signaling, as short hairpin RNA-mediated ablation of c-Met significantly attenuated the response. Furthermore, inhibition of Ras-phosphoinositide 3-kinase (PI3K) signaling impaired the migration of human neural stem cells (hNSCs) toward HGF and other growth factors. Migration toward tumor cells is a highly regulated process, in which multiple growth factor signals converge on Ras-PI3K, causing direct modification of the cytoskeleton. The signaling pathways that regulate hNSC migration are similar to those that promote unregulated glioma invasion, suggesting shared cellular mechanisms and responses. Disclosure of potential conflicts of interest is found at the end of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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