Interaction between Intestinal Parasites and the Gut Microbiota: Implications for the Intestinal Immune Response and Host Defence
Bibliographic record
Abstract
Intestinal parasites, including helminths and protozoa, account for a significant portion of the global health burden. The gastrointestinal (GI) tract not only serves as the stage for these parasitic infections but also as the residence for millions of microbes. As the intricacies of the GI microbial milieu continue to unfold, it is becoming increasingly apparent that the interactions between host, parasite, and resident microbes help dictate parasite survival and, ultimately, disease outcomes. Across both clinical and experimental models, intestinal parasites have been shown to impact microbial composition and diversity. Reciprocally, microbes can directly influence parasitic survival, colonization and expulsion. The gut microbiota can also indirectly impact parasites through the influence and manipulation of the host. Studying this host-parasite-microbiota axis may help bring about novel therapeutic strategies for intestinal parasitic infection as well as conditions such as inflammatory bowel disease (IBD). In this review, we explore the relationship between intestinal parasites, with a particular focus on common protozoa and helminths, and the gut microbiota, and how these interactions can influence the host defence and intestinal immune response. We will also explore the impact of this tripartite relationship in a clinical setting and its broader implications for human health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".