Antigen presentation and dendritic cell biology in malaria
Bibliographic record
Abstract
Dendritic cells (DCs) are important both in amplifying the innate immune response and in initiating adaptive immunity and shaping the type of T helper (Th) response. Although the role of DCs in immune responses to many intracellular pathogens has been delineated and research is underway to identify the mechanisms involved, relatively little is known concerning the role of DCs in immunity to malaria. In this review, we provide an overview and summary of previous and current studies aimed to investigate the role of DCs as antigen presenting cells (APCs). In addition, the role of DCs in inducing innate and adaptive immunity to blood-stage malaria is discussed and, where information is available, the mechanisms involved are presented. Data from studies in humans infected with Plasmodium falciparum, the major human parasite responsible for the high morbidity and mortality associated with malaria throughout many regions of the developing world, as well as data from experimental mouse models are presented. Overall, the data from these studies are conflicting. The possible reasons for these differences, including the use of different parasite species and parasite strains in the mouse studies, are discussed. Nevertheless, together the data have important implications for development of an effective malaria vaccine since the selection of appropriate Plasmodium antigens and/or adjuvants, targeting innate immune responses involving DCs, may provide optimal protection against malaria. It is hoped that this review promotes more investigation among malariologists and immunologists alike on DCs and malaria.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".