Optimization of a semi-nested multiplex PCR to identify Plasmodium parasites in wild-caught Anopheles in Bolivia, and its application to field epidemiological studies
Bibliographic record
Abstract
Without an adequate DNA extraction protocol, the identification of Plasmodium species in whole mosquitoes by PCR is difficult because of the presence of reaction inhibitors from the insects. In this study, eight DNA extraction protocols were tested, from which a chelex-based protocol was selected. Then a semi-nested multiplex PCR technique that detects and distinguishes among the four human Plasmodium species in single mosquitoes and in pools of up to 100 mosquitoes was optimized. The technique was used to detect P. vivax in wild-caught Anopheles pseudopunctipennis from a village in the Andean valleys of Bolivia in May 2003. The prevalence of infection was 0.9%. This is the first direct evidence of P. vivax transmission by this vector in this country. The extraction and PCR technique presented here can be useful to: (1) estimate Plasmodium prevalence in Anopheles populations in low prevalence areas where large numbers of individual mosquitoes would need to be processed to obtain a reliable estimate; (2) incriminate Anopheles species as malaria vectors; (3) identify all the circulating Plasmodium species in vectors from an area; (4) detect mixed infections in mosquitoes; and (5) detect mosquitoes with low-level parasite infections.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".