Tumor necrosis factor-α–induced accentuation in cryoinjury: mechanisms <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Cryosurgical treatment of solid cancer can be greatly assisted by further translation of our finding that a cytokine adjuvant tumor necrosis factor-alpha (TNF-alpha) can achieve complete cancer destruction out to the intraoperatively imaged iceball edge (-0.5 degrees C) over the current clinical recommendation of reaching temperatures lower than -40 degrees C. The present study investigates the cellular and tissue level dose dependency and molecular mechanisms of TNF-alpha-induced enhancement in cryosurgical cancer destruction. Microvascular endothelial MVEC and human prostate cancer LNCaP Pro 5 (LNCaP) cells were frozen as monolayers in the presence of TNF-alpha. Normal skin and LNCaP tumor grown in a nude mouse model were also frozen at different TNF-alpha doses. Molecular mechanisms were investigated by using specific inhibitors to block nuclear factor-kappaB-mediated inflammatory or caspase-mediated apoptosis pathways. The amount of cryoinjury increased in a dose-dependent manner with TNF-alpha both in vitro and in vivo. MVEC were found to be more cryosensitive than LNCaP cells in both the presence and the absence of TNF-alpha. The augmentation in vivo was significantly greater than that in vitro, with complete cell death up to the iceball edge in tumor tissue at local TNF-alpha doses greater than 200 ng. The inhibition assays showed contrasting results with caspase-mediated apoptosis as the dominant mechanism in MVEC in vitro and nuclear factor-kappaB-mediated inflammatory mechanisms within the microvasculatures the dominant mechanism in vivo. These results suggest the involvement of endothelial-mediated injury and inflammation as the critical mechanisms in cryoinjury and the use of vascular-targeting molecules such as TNF-alpha to enhance tumor killing and achieve the clinical goal of complete cell death within an iceball.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".