Bibliographic record
Abstract
The development of synovial sarcoma, a translocationally defined soft tissue tumor of unknown histogenesis with considerable resistance to systemic therapy and a poor prognosis, may involve cancer stem-like cells. Recent studies suggest that synovial sarcoma arises from a primitive progenitor-type cell and have shown that synovial sarcomas contain subpopulations with enhanced tumorigenic potential; however, little is known about cancer stem-like cells in synovial sarcoma. Histologic and gene expression studies have reported features of synovial sarcoma that are reminiscent of neural development, suggesting that a neural cancer stem-like cell marker, such as CD133, may mark cancer stem-like cells in synovial sarcoma, allowing for further characterization. Here, the immunohistochemical expression of CD133 in primary synovial sarcoma tumor tissue and synovial sarcoma cell lines is determined. Subpopulations of CD133 expressing cells are present in all primary synovial sarcomas (5/5) and synovial sarcoma cell lines (3/3) examined. Histologically, CD133 positive cells are dispersed and seem to have dendritic processes. This study demonstrates the presence of CD133 expressing cells in synovial sarcoma for the first time and validates 3 synovial sarcoma cell lines as models for further study of the CD133+ subpopulation. The relationship between CD133 expression and cancer stem-like cells suggests that CD133 expressing synovial sarcoma cells may represent cancer stem-like cells in synovial sarcoma, which has significant implications for understanding the pathogenesis of this tumor and developing effective therapies.
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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 | 0.001 | 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".