Histological observations on the microenvironment of osteolytic bone metastasis by breast carcinoma cell line
Bibliographic record
Abstract
Bone tissue, with its dynamic microenvironment featuring osteoclastic bone resorption, angiogenesis and matrix degradation, appears to facilitate proliferation of tumor cells after the onset of bone metastasis. In this study, we examined metastatic lesions in the femora of BALB/c nu/nu mice two weeks after intracardiac injection with human breast carcinoma MDA-231 cells. Histopathological observations showed the metastatic lesions close to the chondro-osseous junction, and revealed MDA-231 cells loosely intermingled with different cell types such as osteoblasts, fibroblastic stromal cells, osteoclasts and endothelial cells. In the metastatic nest, many tartrate resistant acid phosphatase (TRAPase)-positive osteoclasts accumulated in direct contact with or were close to alkaline phosphatase (ALPase)- or receptor activator of NF-kappaB ligand (RANKL)-positive osteoblastic cells. It seems likely that osteoclastogenesis is mediated through cell-to-cell contacts with ALPase- and RANKL-expressing osteoblastic cells. Formation of many capillaries lacking complete basal membranes and pericytes ratified the results of in situ hybridization, which revealed intense expression of VEGF in tumor nests, and therefore, indicated ongoing tumor-induced angiogenesis. The tumor cells possessed matrix metallo-proteinases (MMPs)-1 and -9, and frequently extended their stout cytoplasmic processes into fragmented fibrillar components of the growth plate cartilage, implicating degradation of cartilaginous matrix. Thus, osteolytic bone metastasis has demonstrated pathological features as tumor-induced angiogenesis and degradation of extracellular matrix, in addition to osteoclastogenesis. This complex interplay between tumor cells and host tissues may enable and nourish the establishment of a microenvironment that facilitates tumor progression.
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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".