Infection and Cancer: Revaluation of the Hygiene Hypothesis
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
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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