The Combined Antitumor Effects of <sup>125</sup> I Radioactive Particle Implantation and Cytokine-Induced Killer Cell Therapy on Xenograft Hepatocellular Carcinoma in a Mouse Model
Bibliographic record
Abstract
The combination of radiotherapy and immunotherapy has shown great promise in eradicating tumors. For example, 125 I radioactive particle implantation and cytokine-induced killer cell therapies have demonstrated efficacy in treating hepatocellular carcinoma. However, the mechanism of this combination therapy remains unknown. In this study, we utilized cytokine-induced killer cells obtained from human peripheral blood mononuclear cells along with 125 I radioactive particle implantation to treat subcutaneous hepatocellular carcinoma xenograft tumors in BALB/c nude mice. The effects of combination therapy on tumor growth, tumor cell apoptosis and proliferation, animal survival, and immune indexes were then assessed. The results indicated that 125 I radioactive particle implantation combined with cytokine-induced killer cells shows a much greater antitumor therapeutic effect than either of the therapies alone when compared to control treatments. Mice treated with a combination of radiotherapy and immunotherapy displayed significantly reduced tumor growth. 125 I radioactive particle implantation upregulated the expression of major histocompatibility complex (MHC) class I chain-related gene A in hepatocellular carcinoma cells and enhanced cytokine-induced killer cell–mediated apoptosis through activation of caspase-3. Furthermore, cytokine-induced killer cells supplied immune substrates to induce a strong immune response after 125 I radioactive particle implantation therapy. In conclusion, 125 I radioactive particle implantation combined with cytokine-induced killer cell therapy significantly inhibits the growth of human hepatocellular carcinoma cells in vivo and improves animal survival times through mutual promotion of antitumor immunity, presenting a promising therapy for hepatocellular carcinoma.
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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".