Bibliographic record
Abstract
An important feature of tumor blood vessels is that they are in a state of constant new tumor blood vessel growth. In endothelial cells, the PI3K/Akt pathway has been shown to play an important role in mediating cell survival, proliferation, and migration. The serine/threonine kinase, mammalian target of rapamycin (mTOR), an important downstream target of PI3K/Akt and mTOR signaling, has been shown to be involved in the control of cell growth and proliferation. To grow beyond a certain size primary tumor and metastases are dependent on the formation of new blood vessels or angiogenesis. In angiogenesis mTOR serves as a central regulator. There is growing evidence in support of the hypothesis that mTOR acts as a critical switch for endothelial cellular catabolism and anabolism, thus determining whether these cells grow and proliferate. This is especially critical in cancer cells bearing disturbances in the TOR pathway. There are several human cancers whereby the PI3K/Akt pathway is dysregulated. Gain or loss mutations of this pathway lead to neoplastic transformation. mTOR inhibitors downregulate hypoxia-inducible factor 1α (HIF1α)-mediated production of pro-angiogenic cytokine, vascular endothelial growth factor (VEGF), by tumor cells and the resulting activation of vascular endothelial growth factor receptors (VEGF-Rs) on endothelial and lymphatic precursor cells inhibiting survival and growth-promoting signals that support tumor vascularization and tumorigenesis. The antiangiogenic and antilymphangiogenic effects of mTOR inhibition may well translate into a reduced incidence of clinically apparent malignancies through reduced tumor growth and lymphatic metastasis, respectively.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".