Macrophages and Plasmacytoid Dendritic Cells Initiate Early Immune Response To Severe Malaria In The Bone Marrow
Bibliographic record
Abstract
Abstract Malaria still remains a significant global health problem worldwide with approximately 200 million people infected, and half a million deaths every year. Most of these fatalities (80%) occur in children under the age of five, and the latest RTS, S, preventative vaccine trial only exhibited low protective efficacy (below 30%). The blood stage of Plasmodium infection accounts for the clinical symptoms of malaria, yet the immune mechanisms underlying disease outcomes are poorly understood. Here we have analyzed a cohort of severely infected Malawian children and revealed dramatic levels of inflammatory cytokines and extremely robust blood leukocyte activation, notably for CD14+CD16lo CCR2+ inflammatory monocytes, plasmacytoid DCs (pDCs), NK and T cells. We could recapitulate these observations using the Plasmodium yoelii (Py) 17X YM surrogate mouse model of lethal blood stage malaria. Using this experimental system, we demonstrate that type I interferon (IFN) signals as a key cytokine controlling lethal outcomes and immune cell activation. Plasmacytoid dendritic cells represented the main cellular source of type I IFN in the bone marrow and the blood of infected mice. Most interestingly, the activation of pDCs required CD169+ macrophages, and both cell types exhibited prolonged interactions in the bone marrow of infected mice by intravital microscopy. We will present additional data uncovering the molecular sensing pathways controlling these processes. Altogether our study establishes essential molecular pathways and cellular interactions that occur during this significant human parasitic infection, and suggest novel potential therapeutic targets involved in severe malaria.
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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".