Recent Advances in Understanding the Inflammatory Response in Malaria: A Review of the Dual Role of Cytokines
Bibliographic record
Abstract
Malaria is a serious and, in some unfortunate cases, fatal disease caused by a parasite of the Plasmodium genus. It predominantly occurs in tropical areas where it is transmitted through the bite of an infected Anopheles mosquito. The pathogenesis of malaria is complex and incompletely elucidated. During blood-stage infection, in response to the presence of the parasite, the host’s immune system produces proinflammatory cytokines including IL-6, IL-8, IFN-γ, and TNF, cytokines which play a pivotal role in controlling the growth of the parasite and its elimination. Regulatory cytokines such as transforming growth factor- (TGF-) β and IL-10 maintain the balance between the proinflammatory and anti-inflammatory responses. However, in many cases, cytokines have a double role. On the one hand, they contribute to parasitic clearance, and on the other, they are responsible for pathological changes encountered in malaria. Cytokine-modulating strategies may represent a promising modern approach in disease management. In this review, we discuss the host immune response in malaria, analyzing the latest studies on the roles of pro- and anti-inflammatory cytokines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".