Parasite-enhanced immunotherapy: transforming the “cold” tumors to “hot” battlefields
Bibliographic record
Abstract
Immunotherapy has emerged as a highly effective treatment for various tumors. However, the variable response rates associated with current immunotherapies often restrict their beneficial impact on a subset of patients. Therefore, more effective treatment approaches that can broaden the scope of therapeutic benefits to a larger patient population are urgently needed. Studies have shown that some parasites and their products, for example, Plasmodium , Toxoplasma , Trypanosoma , and Echinococcus , can effectively transform "cold" tumors into "hot" battlefields and reshape the tumor microenvironment, thereby stimulating innate and adaptive antitumor immune responses. These parasitic infections not only achieve the functional reversal of innate immune cells, such as neutrophils, macrophages, myeloid-derived suppressor cells, regulatory T cells, and dendritic cells, in tumors but also successfully activate CD4 + /CD8 + T cells and even B cells to produce antibodies, ultimately resulting in an antitumor-specific immune response and antibody-dependent cellular cytotoxicity. Animal studies have confirmed these findings. This review discusses the abovementioned content and the challenges faced in the future clinical application of antitumor treatment strategies based on parasitic infections. With the potential of these parasites and their byproducts to function as anticancer agents, we anticipate that further investigations in this field could yield significant advancements in cancer treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".