Contact-activated migration of melanoma B16 and sarcoma XC cells
Bibliographic record
Abstract
During migration, tumour cells interact with neighbouring neoplastic and normal host cells, and such interaction may influence their motile activity. We investigated the effect of homotypic collisions on the motile activity of two tumour cell lines, mouse melanoma B16 and rat sarcoma XC, and nontransformed human skin fibroblasts. It was found that the tumour cells show only limited motile activity when moving as single cells without contact with neighbours. At a higher density of the culture (and also at a greater number of cell to cell contacts) the activation of motility of investigated tumour cells was observed. On the other hand, the normal human skin fibroblasts showed a typical reaction of density-dependent inhibition of motility. The motile activity of tumour cells was not affected by conditioned media and was visibly dependent on a direct physical contact among colliding cells. The activation of cell movement was observed about 40-50 min after the initial contact between tumour cells. Contact-activated migration of neoplastic cells was inhibited by 50 microM verapamil (a selective voltage-gated calcium channel inhibitor) and 10 microM gadolinium chloride (a nonspecific blocker of mechanosensitive ion channels) but not by pertussis toxin. The observation that homotypic collisions among tumour cells strongly increase their motile activity suggests that contact-activated migration may play a significant role in tumour invasion and metastasis.
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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) | 0.000 | 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) | 0.001 | 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".