Combined radiation therapy and dendritic cell vaccine for treating solid tumors with liver micro‐metastasis
Bibliographic record
Abstract
BACKGROUND: Tumor metastasis and relapse are major obstacles in combating human malignant diseases. Neither radiotherapy alone nor injection of dendritic cells (DCs) can successfully overcome this problem. Radiation induces tumor cell apoptosis and necrosis, resulting in the release of tumor antigen and danger signals, which are favorable for DC capturing antigens and maturation. Hence, the strategy of combined irradiation and DC vaccine may be a novel approach for treating human malignancies and early metastasis. METHODS: To develop an effective combined therapeutic approach, we established a novel concomitant local tumor and liver metastases model through subcutaneous (s.c.) and intravenous (i.v.) injection. We selected the optimal time for DC injection after irradiation and investigated the antitumor effect of combining irradiation with DC intratumoral injection and the related mechanism. RESULTS: Combined treatment with radiotherapy and DC vaccine could induce a potent antitumor immune response, resulting in a significant decrease in the rate of local tumor relapse and the numbers of liver metastases. The related mechanisms for this strong antitumor immunity of this combined therapy might be associated with the production of apoptotic and necrotic tumor antigens and heat shock proteins after irradiation, phagocytosis, migration and maturation of DCs, and induction of more efficient tumor-specific cytotoxic T lymphocyte activity through a cross-presentation pathway. CONCLUSIONS: Co-administration of local irradiation and intratumoral DC injection may be a promising strategy for treating radiosensitive tumors and eliminating metastasis in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".