Human Immune Response to Mosquito-Borne Pathogens: Mechanisms and Implications
Bibliographic record
Abstract
Mosquito-borne diseases, such as malaria, dengue, and Zika, pose a significant global health threat due to their widespread prevalence and severe consequences. This study investigates the intricate mechanisms of human immune responses to mosquito-borne pathogens, focusing on both innate and adaptive immunity. The role of pattern recognition receptors, inflammatory pathways, and cytokine signaling in initial pathogen detection is explored, alongside the limitations of innate immunity. The adaptive immune response, encompassing B cell-mediated antibody production and T cell functionality, is analyzed, with attention to the challenges posed by immunopathology. A case study on Plasmodium highlights the immune evasion strategies employed by pathogens, emphasizing antigenic variation and immune suppression. The implications of these findings for vaccine development and therapeutic interventions are discussed, underscoring current progress and challenges in achieving long-term immunity. Finally, the study identifies future research directions, advocating for systems immunology and innovative technologies to enhance our understanding of host-pathogen interactions. This research provides a comprehensive framework for leveraging immune responses in combating mosquito-borne diseases and improving global health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".