Macrophage Biology and Their Activation by Protozoan-Derived Glycosylphosphatidylinositol Anchors and Hemozoin
Bibliographic record
Abstract
Despite recent advances in medical technology and a global effort to improve public health and hygiene, parasitic infections remain a major health and economic burden worldwide. The World Health Organization estimates that about 1/3 of the world's population is currently infected with a soil-transmitted helminth, and millions more suffer from diseases caused by protozoan parasites including Plasmodium, Trypanosoma, and Leishmania species. Due to the selective pressure applied by parasitic and other infections, animals have evolved an intricate immune system; however, the current worldwide prevalence of parasitic infections clearly indicates that these pathogens have adapted equally well. Thus, developing a better understanding of the host-parasite relationship, particularly by focusing on the host immune response and the mechanisms by which parasites evade this response, is a critical first step in mitigating the detrimental effects of parasitic diseases. Macrophages are critical contributors during the host response to protozoan parasites, and the success or failure of these cells often tips the balance in favor of the host or parasite. Herein, we briefly discuss macrophage biology and provide an update on our current understanding of how these cells recognize glycosylphosphatidylinositol anchors from protozoan parasites as well as malarial hemozoin.
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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 | 0.001 |
| 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 | 0.001 |
| 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".