Building the Perfect Parasite: Cell Division in Apicomplexa
Bibliographic record
Abstract
Apicomplexans are pathogens responsible for malaria, toxoplasmosis, and crytposporidiosis in humans, and a wide range of livestock diseases. These unicellular eukaryotes are stealthy invaders, sheltering from the immune response in the cells of their hosts, while at the same time tapping into these cells as source of nutrients. The complexity and beauty of the structures formed during their intracellular development have made apicomplexans the darling of electron microscopists. Dramatic technological progress over the last decade has transformed apicomplexans into respectable genetic model organisms. Extensive genomic resources are now available for many apicomplexan species. At the same time, parasite transfection has enabled researchers to test the function of specific genes through reverse and forward genetic approaches with increasing sophistication. Transfection also introduced the use of fluorescent reporters, opening the field to dynamic real time microscopic observation. Parasite cell biologists have used these tools to take a fresh look at a classic problem: how do apicomplexans build the perfect invasion machine, the zoite, and how is this process fine-tuned to fit the specific niche of each pathogen in this ancient and very diverse group? This work has unearthed a treasure trove of novel structures and mechanisms that are the focus of this review.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".