Ultrasensitive loop mediated isothermal amplification (US-LAMP) to detect malaria for elimination
Bibliographic record
Abstract
BACKGROUND: Malaria elimination requires diagnostic methods able to detect parasite levels well below what is currently possible with microscopy and rapid diagnostic tests. This is particularly true in surveillance of malaria at the population level that includes so-called "asymptomatic" individuals. METHODS: The development of the first ultrasensitive loop mediated amplification method capable of detecting malaria from both whole blood and dried blood spots (DBS) is described. The 18S rRNA and corresponding genes that remain stable on DBS for up to 5 months are targeted. RESULTS: In the case of Plasmodium falciparum, lower limits of detection of 25 parasite/mL and 50-100 parasite/mL from whole blood and DBS were obtained, respectively. A sensitivity of 97.0% (95% CI 82.5-99.8) and specificity of 99.1% (95% CI 97.6-99.7) was obtained for the detection of all species in asymptomatic individuals from Africa and Asia (n = 494). CONCLUSION: This tool is ideally suited for low middle-income countries where malaria is endemic and ultrasensitive surveillance of malaria is highly desirable for elimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".