Noninvasive Approach of <i>Plasmodium falciparum</i> Molecular Detection for Malaria Surveillance in Malaria Endemic Areas in Cameroon
Bibliographic record
Abstract
Background. Accurate, cost‐effective, and noninvasive alternative molecular methods are needed for detecting low malaria parasitemia. The currently‐used nested polymerase chain reaction (nPCR) requires blood as well as skilled personnel in order to minimise the risk of bloodborne disease transmission. Therefore, this study is aimed at assessing the accuracy of a noninvasive and more affordable malaria diagnosis with saliva using the loop‐mediated isothermal amplification (LAMP) technique. Methods. A cross‐sectional study was conducted in the Centre and Southwest regions of Cameroon. Matched blood and saliva samples collected from symptomatic and asymptomatic participants were tested for malaria using rapid diagnostic tests, microscopy, PCR, and LAMP. Statistics were performed using R studio software at 95% confidence interval. Results . A total of 100 participants (65% symptomatic and 35% asymptomatic) aged between 1 and 74 years with a balanced gender distribution ratio of 1.08 were included in our study. The prevalence of malaria was 61%, 57%, 59%, 42%, 35%, 17%, and 16% for blood‐RDT, blood‐PCR, blood‐LAMP, blood‐RT‐LAMP, saliva‐PCR, saliva‐RT‐LAMP, and saliva‐LAMP, respectively. Both saliva and blood showed a sensitivity of 43.90% and respective specificities of 68.75% and 57.62%. When using RT‐LAMP, sensitivities of 49.38% and 48.21% and specificities of 94.11% and 66.67% were recorded for saliva and blood, respectively. Sensitivities of 70.23% and 73.49% and specificities of 62.5% and 76.47% were recorded, respectively, for saliva‐LAMP and saliva‐RT‐LAMP when compared to saliva‐PCR as the gold standard. Saliva‐LAMP and saliva‐RT‐LAMP had a fair agreement ( к = 0.221 and 0.352, respectively) with saliva‐PCR. Homemade LAMP and RT‐LAMP technologies match the WHO recommendations and after proper validation in a larger sample size, could serve for malaria diagnosis in developing countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".