Stromal Integrin α11β1 Affects RM11 Prostate and 4T1 Breast Xenograft Tumors Differently
Bibliographic record
Abstract
PURPOSE: It has been implied that the collagen binding integrin α11β1 plays a role in carcinogenesis. As still relatively little is known about how the stromal integrin α11β1 affects different aspects of tumor development, we wanted to examine the direct effects on primary tumor growth, fibrosis, tumor interstitial fluid pressure (PIF) and metastasis in murine 4T1 mammary and RM11 prostate tumors, using an in vivo SCID integrin α11-deficient mouse model. METHODS: Tumor growth was measured using a caliper, PIF by the wick-in-needle technique, activated fibroblasts by α-SMA immunofluorescence staining and fibrosis by transmission electron microscopy and picrosirius-red staining. Metastases were evaluated using hematoxylin and eosin stained sections. RESULTS: RM11 tumor growth was significantly reduced in the SCID integrin α11-deficient (α11-KO) compared to in SCID integrin α11 wild type (WT) mice, whereas there was no similar effect in the 4T1 tumor model. The 4T1 model demonstrated an alteration in collagen fibril diameter in the integrin α11-KO mice compared to WT, which was not found in the RM11 model. There were no significant differences in the amount of activated fibroblasts, total collagen content, collagen organization or PIF in the tumors in integrin α11-deficient mice compared to WT mice. There was also no difference in lung metastases between the two groups. CONCLUSION: Deficiency of stromal integrin α11β1 showed different effects on tumor growth and collagen fibril diameter depending on tumor type, but no effect on tumor PIF or development of lung 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) | 0.001 | 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) | 0.002 | 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".