Infection and Cancer: Revaluation of the Hygiene Hypothesis
Bibliographic record
Abstract
Several studies have shown that persistent infections and inflammation can favor carcinogenesis. At the same time, certain types of pathogens and antitumor immune responses can decrease the risk of tumorigenesis or lead to cancer regression. Infectious agents and their products can orchestrate a wide range of host immune responses, through which they may positively or negatively modulate cancer development and/or progression. The factors that direct this dichotomous influence of infection-mediated immunity on carcinogenesis are not well understood. Even though not universal, several previous reports have investigated the inverse link of pathogen-induced "benign" inflammation to carcinogenesis and various other pathologies, ranging from autoimmune diseases to allergy and cancer. Several models and ideas are discussed in this review, including the impact of decreased exposure to pathogens, as well as the influence of pathogen load, the timing of infection, and the type of instigated immune response on carcinogenesis. These phenomena should guide future investigations into identifying novel targets within the microbial and host proteome, which will assist in the development of cancer therapeutics and vaccine remedies, analogous to earlier efforts based on helminthic components for the prevention and/or treatment of several pathologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".