Bibliographic record
Abstract
Historically, a hallmark of tumorigenesis was the ability to grow in an anchorage-independent manner. Hence, tumors were thought to proliferate and survive independently of integrin attachment to the substratum. However, recent data suggest that integrins regulate not only tumor cell proliferation, survival and migration, but may also influence their response to anti-cancer agents. Interestingly, these influences are largely masked by growth of tumor cells in the standard, yet artificial, environment of 2D cell culture, but are readily apparent under 3D in vitro culture conditions and in tumor growth in vivo. We, and others, have recently demonstrated that the β1 integrin subunit controls the growth and invasion of prostate tumor cells in 3D culture conditions. Recently, the importance of integrins has also been demonstrated using tissue specific conditional knockout strategies in transgenic mouse tumor models, where they control primary tumor growth and dictate the site of metastatic spread. Furthermore, integrin-extracellular matrix interactions may modulate the response of tumors to standard chemotherapy agents or radiation. Taken together, these results highlight the important role of integrins in regulating tumor growth and metastasis; however, point out that the evaluation of their contribution to these processes requires appropriate contextual modeling.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".