Time-Lapse Imaging of Red Blood Cell Invasion by the Rodent Malaria Parasite Plasmodium yoelii
Bibliographic record
Abstract
In order to propagate within the mammalian host, malaria parasites must invade red blood cells (RBCs). This process offers a window of opportunity in which to target the parasite with drugs or vaccines. However, most of the studies relating to RBC invasion have analyzed the molecular interactions of parasite proteins with host cells under static conditions, and the dynamics of these interactions remain largely unstudied. Time-lapse imaging of RBC invasion is a powerful technique to investigate cell invasion and has been reported for Plasmodium knowlesi and Plasmodium falciparum. However, experimental modification of genetic loci is laborious and time consuming for these species. We have established a system of time-lapse imaging for the rodent malaria parasite Plasmodium yoelii, for which modification of genetic loci is quicker and simpler. We compared the kinetics of RBC invasion by P. yoelii with that of P. falciparum and found that the overall kinetics during invasion were similar, with some exceptions. The most striking of these differences is that, following egress from the RBC, the shape of P. yoelii merozoites gradually changes from flat elongated ovals to spherical bodies, a process taking about 60 sec. During this period merozoites were able to attach to and deform the RBC membrane, but were not able to reorient and invade. We propose that this morphological change of P. yoelii merozoites may be related to the secretion or activation of invasion-related proteins. Thus the P. yoelii merozoite appears to be an excellent model to analyze the molecular dynamics of RBC invasion, particularly during the morphological transition phase, which could serve as an expanded window that cannot be observed in P. falciparum.
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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".