The bifacial role of helminths in cancer: Involvement of immune and non-immune mechanisms
Bibliographic record
Abstract
Infectious agents have been associated with cancer due to activation of pro-carcinogenic inflammatory processes within their host. Several reports, however, indicate that specific pathogens may be able to elicit anti-tumor immune responses that can lead to protection from tumorigenesis or cancer regression. Amongst these "beneficial" pathogens are some helminthic parasites that have already been connected with prevention of autoimmune diseases and allergies, immune conditions increasingly associated with cancer. Even though helminths have co-existed with humans and their ancestors for millions of years, investigations of their impact on human (patho)physiology are relatively new and the functions of components that can explain the helminth bi-directional influence on carcinogenesis are not well understood. This review aims to discuss evidence for the helminth-induced immune, genetic, epigenetic, proteomic, hormonal and metabolic changes that may ultimately mediate the potential pro- or anti-carcinogenic role of helminths. This overview may serve future investigations in clarifying the tumorigenic role of the most common helminthic parasites. It may also inspire the development of anti-cancer regimens and vaccines, in parallel to ongoing efforts of using helminth-based components for the prevention and/or treatment of autoimmune diseases and allergies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".