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 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) | 0.001 | 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 | 0.001 |
| 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".