Low dose intradermal infection with <i>Trypanosoma congolense</i> leads to expansion of regulatory T cells and enhanced susceptibility to reinfection (P1055)
Bibliographic record
Abstract
Abstract BALB/c mice are highly susceptible to experimental T. congolense infection. Interestingly, a recent report showed that these mice are relatively resistant to primary intradermal low dose infection. Paradoxically, repeated low dose intradermal infections predispose to enhanced susceptibility to an otherwise non-infectious dose challenge. Here, we explored the mechanisms responsible for this low-dose-induced susceptibility to subsequent low dose reinfection. We found that akin to intraperitoneal infection, low dose intradermal infection leads to production of IL-10, IL-6, IL-12, TGF-β and IFN-γ by spleen and draining lymph node cells. Interestingly, despite the absence of parasitemia, low dose intradermal infection led to expansion of CD4+CD24+Foxp3+ cells (Tregs) in both the spleens and lymph nodes draining the infection site. Depletion of Tregs by anti-CD25 mAb treatment during primary or before reinfection following repeated low dose infection completely abolished the low dose-induced enhanced susceptibility to rechallenge infection. In addition, Tregs depletion was associated with dramatic reduction in serum levels of TGF-β and IL-10. Collectively, these findings show that low dose intradermal infection leads to rapid expansion of Tregs and these cells mediate enhanced susceptibility to subsequent infection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".