A Lab‐On‐chip Tool for Rapid, Quantitative, and Stage‐selective Diagnosis of Malaria
Bibliographic record
Abstract
Abstract Malaria remains the most important mosquito‐borne infectious disease worldwide, with 229 million new cases and 409.000 deaths in 2019. The infection is caused by a protozoan parasite which attacks red blood cells by feeding on hemoglobin and transforming it into hemozoin. Despite the WHO recommendation of prompt malaria diagnosis, the quality of microscopy‐based diagnosis is frequently inadequate while rapid diagnostic tests based on antigens are not quantitative and still affected by non‐negligible false negative/positive results. PCR‐based methods are highly performant but still not widely used in endemic areas. Here, a diagnostic tool (TMek), based on the paramagnetic properties of hemozoin nanocrystals in infected red blood cells (i‐RBCs), is reported on. Exploiting the competition between gravity and magnetic forces, i‐RBCs in a whole blood specimen are sorted and electrically detected in a microchip. The amplitude and time evolution of the electrical signal allow for the quantification of i‐RBCs (in the range 10–105 i‐RBC µL−1) and the distinction of the infection stage. A preliminary validation study on 75 patients with clinical suspect of malaria shows on‐field operability, without false negative and a few false positive results. These findings indicate the potential of TMek as a quantitative, stage‐selective, rapid test for malaria.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".