High-efficiency enrichment enables identification of aptamers to circulating Plasmodium falciparum-infected erythrocytes
Bibliographic record
Abstract
Abstract Plasmodium falciparum is the causative agent of the deadliest human malaria. New molecules are needed that can specifically bind to erythrocytes that are infected with P. falciparum for diagnostic purposes, to disrupt host-parasite interactions, or to deliver chemotherapeutics. Aptamer technology has the potential to revolutionize biological diagnostics and therapeutics; however, broad adoption is hindered by the high failure rate of the systematic evolution of ligands by exponential enrichment (SELEX). Here we performed parallel SELEX experiments to compare the impact of two different methods for single-strand recovery on the efficiency of aptamer enrichment. Our experimental results and analysis of SELEX publications spanning 13 years implicate the alkaline denaturation step as a significant cause for inefficient aptamer selection. Thus, we applied an exonuclease single-strand recovery step in our SELEX to direct aptamers to the surface of erythrocytes infected with P. falciparum. The selected aptamers bind with high affinity (low nanomolar Kd values) and selectivity to exposed surface proteins of both laboratory parasite strains as well isolates from patients in Asia and Africa with clinical malaria. The results obtained in this study potentially open new approaches to malaria diagnosis and surveillance.
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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 | 0.001 | 0.001 |
| 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".