Mapping the expression of endothelial adhesion receptors for <i>Plasmodium falciparum</i> -infected erythrocytes in fatal cerebral malaria in Malawian children
Bibliographic record
Abstract
We investigated the expression and distribution of 5 cytoadhesion receptors for the Plasmodium falciparum erythrocyte membrane protein 1 in 12 regions of post-mortem brains of 50 Malawian children, that is, 27 with the clinical and pathological diagnosis of cerebral malaria (CM) and 23 with a non-malarial cause of death. We quantified the expression of each receptor by microvascular endothelium and the colocalization of receptor-expressing microvessels with sequestered infected red blood cells (iRBC) and calculated a receptor-independent sequestration ratio. There were differences in the level of expression and regional distribution of the five receptors: ICAM-1 was the most widely expressed receptor, followed by CD36, VCAM-1, E-selectin, and thrombospondin. Receptor-expressing microvessels were most numerous in the frontal lobe and least numerous in the brainstem and cerebellum. Colocalization of receptor-expressing endothelial cells with iRBC was present in all brain regions; it was highest for ICAM-1 and CD36 and greatest in the frontal lobe. The sequestration ratios were close to 100% for all receptors across all brain regions and were similar in cerebral and extracerebral microvessels. Receptor expression and colocalization ratios were greater in the brain than in the lung, heart, liver, spleen, and subcutaneous tissue. These differences in cerebral endothelial expression of cytoadhesion receptors and their preferential regional distribution may underpin differences in iRBC sequestration and lesion development in CM. Moreover, greater expression of these receptors in the brain vs peripheral organs may explain a comparatively greater degree of iRBC sequestration in the brain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".