Parasite-enhanced immunotherapy: transforming the “cold” tumors to “hot” battlefields
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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) | 0.000 | 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 it