Thermal necrosis in orthopedic bone tumors: experimental research
Bibliographic record
Abstract
Introduction: The extent of surgical resection in orthopedic oncology differs according to tumor biology. While malignant bone tumors are operatively managed with wide resection, benign bone tumors and metastatic carcinomas are often treated through intralesional excision and adjuvant modalities, including the elimination of residual neoplastic cells through thermal necrosis. This study investigates in vitro temperature thresholds for thermal necrosis in common orthopedic bone tumors. Methodology: Eleven cell lines, including metastatic carcinomas to bone (A549, A498, FU-UR-1, PC3, MDA-MB-231, TT, MCF7, and K1), giant cell tumor of bone, osteosarcoma (HG-63), and control non-neoplastic cells (HEK293) were cultured. Cells were exposed to thermal stress at varying times and temperatures and evaluated for survival and viability with crystal violet and MTT assays. Results: Both the MTT and crystal violet assay demonstrated statistically superior rates of viability and survival for A549 (lung carcinoma), FU-UR-1 (renal carcinoma), K1 (thyroid carcinoma), and MG-63 (osteosarcoma) cell lines compared to control (HEK293 cells) at 60°C. Additionally, the MTT assay demonstrated superior viability for PC3 (prostate carcinoma), MCF7 (breast carcinoma), and A498 (renal carcinoma) compared to control. All cell lines demonstrated significantly decreased survival and viability in temperatures more than 90°C. Conclusion: This study demonstrated in vitro thresholds for thermal necrosis for cell lines of common orthopedic tumors of bone. The A549 (lung carcinoma), K1 (thyroid carcinoma), and FU-UR-1 (renal carcinoma) cell lines demonstrated greater resistance to heat stress compared to non-neoplastic control cells. Temperatures in excess of 90°C are necessary to reliably reduce cell survival and viability to less than 10%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".