Induction of immune tolerance through an IL‐10 dependent mechanism allows Entamoeba histolytica successful cololization in the colon
Bibliographic record
Abstract
Entamoeba histolytica infects 10% of the world's population, which can lead to amoebic dysentery and/or liver abscess. The majority of infected individuals are completely asymptomatic. We hypothesized that E. histolytica is capable of inducing immune tolerance in antigen presenting cells thus promoting parasite survival in the colon in the absence of an overt inflammatory or immune response. In support of this, we show that dendritic cells (DC's) and macrophages (mφ) exposed to amoeba secreted proteins can induce a significantly up‐regulation of IL‐10 mRNA expression and increase phosphorylation of ILT2. Furthermore, low concentrations (10μg/mL) of amoebic proteins caused rapid 3–8‐fold up‐regulation of ILT2 inhibitory receptor on CD4 + T cells in the presence of mφ or DCs 36 h following exposure. However, there was no significant up‐regulation of ILT2 if T cells were deficient in IL‐10. Moreover, amoebic proteins caused a significantly increased in apoptosis in IL‐10 deficient T cells after 48 or 72 hr as compared to wild type controls. We conclude that E. histolytica is capable of inducing IL‐10 production in antigen presenting cells, which is required for up‐regulating ILT2 on CD4 + T cells and inhibition of T cells apoptosis. This work was supported by NSERC. JMR is a recepient of the QE II Graduate Scholarship from the Province of Alberta and CAG/CIHR studentship.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".