Real-Time PCR Assay for Rapid Detection and Analysis of PfCRT Haplotypes of Chloroquine-Resistant <i>Plasmodium falciparum</i> Isolates from India
Bibliographic record
Abstract
Chloroquine-resistant Plasmodium falciparum (CRPF) malaria isolates in Southeast Asia and sub-Saharan Africa share the same Plasmodium falciparum chloroquine resistance transporter (PfCRT) haplotype (CVIET; amino acids 72 to 76). It is believed that CRPF malaria emerged in Southeast Asia and spread to sub-Saharan Africa via the Indian subcontinent. Based on this assumption, we hypothesized that CRPF isolates in India should possess the same drug resistance haplotype (PfCRT haplotype CVIET) as P. falciparum isolates in Southeast Asia and Africa and that the prevalence of CRPF may be higher and more widespread in India than appreciated. To test this postulate, we utilized a standardized real-time PCR assay to assess the prevalence and distribution of PfCRT haplotypes in P. falciparum isolates (n = 406) collected from Western, Central, and Eastern states in India and compared them to isolates from South America and Africa. Based on the proportion of isolates possessing the molecular marker K76T, the prevalence of chloroquine resistance was high in all five regions of India studied (91%), as well as in Uganda (98%) and Suriname (100%). All isolates from Suriname contained the chloroquine-resistant SVMNT haplotype typical of South American isolates, and 98% of isolates from Uganda possessed the chloroquine-resistant CVIET haplotype characteristic of Southeast Asian/African strains. However, of 246 P. falciparum isolates from across India that contained the molecular marker for chloroquine resistance, 81% contained the SVMNT haplotype. In conclusion, the prevalence of CRPF malaria was high in geographically dispersed regions of India, and the primary haplotype observed, SVMNT, did not support a presumed geographic spread from contiguous Southeast Asia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".