Visualization of <i>Plasmodium falciparum–</i> Endothelium Interactions in Human Microvasculature
Bibliographic record
Abstract
Plasmodium falciparum-infected erythrocytes roll on and/or adhere to CD36, intercellular adhesion molecule (ICAM)-1, vascular cell adhesion molecule (VCAM)-1, and P-selectin under shear conditions in vitro. However, the lack of an adequate animal model has made it difficult to determine whether infected erythrocytes do indeed interact in vivo in microvessels. Therefore, we made use of an established model of human skin grafted onto severe combined immunodeficient (SCID) mice to directly visualize the human microvasculature by epifluorescence intravital microscopy. In all grafts examined, infected erythrocytes were observed to roll and/or adhere in not just postcapillary venules but also in arterioles. In contrast, occlusion of capillaries by infected erythrocytes was noted only in approximately half of the experiments. Administration of an anti-CD36 antibody resulted in a rapid reduction of rolling and adhesion. More importantly, already adherent cells quickly detached. The residual rolling after anti-CD36 treatment was largely inhibited by an anti-ICAM-1 antibody. Anti-ICAM-1 alone reduced the ability of infected erythrocytes to sustain rolling and subsequent adhesion. These findings provide conclusive evidence that infected erythrocytes interact within the human microvasculature in vivo by a multistep adhesive cascade that mimics the process of leukocyte recruitment.
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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) | 0.001 | 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".