Unveiling the silent threat: Investigating asymptomatic plasmodium infections in Gorgora, Ethiopia through microscopy and loop-mediated isothermal amplification
Bibliographic record
Abstract
BACKGROUND: The asymptomatic carriers of the Plasmodium parasite represent a large hidden reservoir of Plasmodium species. These individuals can carry microscopically detectable levels and sub-microscopic levels of Plasmodium parasitemia. As a result, the use of clinical diagnostic methods, such as Rapid Diagnosis Tests (RDTs) and Giemsa microscopy leads to underestimation of the burden of asymptomatic malaria. Thus, the use of highly sensitive molecular methods such as loop-mediated isothermal amplification techniques (LAMP) helps to overcome these limitations and is crucial to reporting the true burden of the disease. OBJECTIVE: This study aimed to determine the prevalence of Asymptomatic Plasmodium infections (APIs), and evaluate light microscopy for the detection of APIs using the LAMP method as a reference in Gorgora, Western Dembia district, Northwest Ethiopia. METHOD: A community-based cross-sectional survey was carried out from May 17 to June 30, 2023, among households located in particular kebeles at Gorgora. The study participants were chosen using a multi-stage sampling technique. One Hundred Fifteen households and one household member from each household were selected using systematic random and simple random sampling techniques, respectively. To gather sociodemographic data, semi-structured questionnaires were used. Capillary blood samples were obtained from each study participant and screened for Plasmodium species infections using a LAMP kit and light microscopy. The data were entered into Epi Data version 4.6 and exported to SPSS version 25 for analysis. RESULTS: The prevalence of APIs through light microscopy examination and LAMP analysis was 6.1% (95%CI: 2.5-12.1) and 11.3% (95%CI: 6.2-18.6) respectively. Using LAMP as a reference, the sensitivity and specificity of microscopy were 53.8% and 100% respectively. Microscopy examination missed six Plasmodium infections that were positive by LAMP analysis. A measure of agreement between LAMP and Microscopy was found to be 0.67(k = 0.67). CONCLUSIONS: A significant proportion of APIs was found which likely act as a reservoir of transmission. This study also showed a significant number of APIs were missed by microscopy techniques. Thus, efforts to control and eliminate malaria should also consider these sub-microscopic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".