Investigating the effect of cGRP78 vaccine against different cancer cells and its role in reducing melanoma metastasis
Bibliographic record
Abstract
Background and purpose: Treatment of malignancies with chemotherapy and surgery is often associated with disease recurrence and metastasis. Immunotherapy improves cancer treatment by creating an active response against tumor antigens. Various cancer cells express a large amount of glucose-regulated protein 78 (GRP78) protein on their surface. Stimulating the immune system against this antigen can expose cancer cells to the immune system. Herein, we investigated the effectiveness of a cGRP78-based vaccine against different cancer cells. Experimental approach: BALB/c mice were immunized with the cGRP78. The humoral immune response against different cancer cells was assessed by Cell-ELISA. The cellular immunity response was determined by splenocyte proliferation assay with different cancer antigens. The effect of vaccination on metastasis was investigated in vaccinated mice by injecting melanoma cancer cells into the tail of mice. Findings/Results: These results indicated that the cGRP78 has acceptable antigenicity and stimulates the immune system to produce antibodies. After three injections, the amount of produced antibody was significantly different from the control group. Compared to the other three cell types, Hela and HepG2 showed the highest reaction to the serum of vaccinated mice. Cellular immunity against the B16F10 cell line had the best results compared to other cells. The metastasis results showed that after 30 days, the growth of B16F10 melanoma cancer cells was not noticeable in the lung tissue of vaccinated mice. Conclusion and implications: Considering the resistance of vaccinated mice to metastasis, this vaccine offers a promising prospect for cancer treatment by inhibiting the spread of cancer cells.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".