<i>Anopheles</i> Mosquito Bites Activate Cutaneous Mast Cells Leading to a Local Inflammatory Response and Lymph Node Hyperplasia
Bibliographic record
Abstract
When Anopheles mosquitoes probe the skin for blood feeding, they inject saliva in dermal tissue. Mosquito saliva is known to exert various biological activities, but its perception by the immune system and its role in parasite transmission remain poorly understood. In the present study, we report on the cellular changes occurring in the mouse skin and draining lymph nodes after a Anopheles stephensi mosquito bite. We show that mosquito bites induce dermal mast cell degranulation, leading to fluid extravasation and neutrophil influx. This inflammatory response does not occur in mast cell-deficient W/W(v) mice, unless these are reconstituted specifically with mast cells. Mast cell activation caused by A. stephensi mosquito bites is followed by hyperplasia of the draining lymph node due to the accumulation of CD3(+), B220(+), CD11b(+), and CD11c(+) leukocytes. The T cell enrichment of the draining lymph nodes results from their sequestration from the circulation rather than local proliferation. These data demonstrate that mosquito bites and very likely saliva rapidly trigger the immune system, emphasizing the critical contribution of peripheral mast cells in inducing T cell and dendritic cell recruitment within draining lymph nodes, a prerequisite for the elicitation of T and B lymphocyte priming.
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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) | 0.002 | 0.001 |
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".