Magneto-Optical Detection of Synthetic Malaria Pigment in Photonic Crystal Fiber
Bibliographic record
Abstract
The necessity to develop new technologies for high-sensitivity malaria diagnosis has sparked a global effort in medical and integrative sciences. Most developing procedures rely on research-grade instruments, sophisticated assays, or on expertise. In this work, we propose an alternative optical methodology using a compact and user-friendly apparatus based on a photonic crystal fiber (PCF). Malaria pigment known as hemozoin is an insoluble reddish brown microcrystalline. These crystallites stand out from other blood components in terms of their exceptional magneto-optical features. Consequently, they can function as spinning entities in suspension in response to the external magnetic field. Here, synthetic hemozoin (SHz) was obtained in a forthright way with a high yield of 75%. In addition, the prepared sample was characterized morphologically and structurally. The PCF’s nanoholes were filled with the aqueous suspension of SHz with various concentrations and transmitted power recorded in response to the magnetic field. We demonstrate a sensor with a detection threshold of 7.2 parasite/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula> well below the level of clinical relevance (50–100 parasite/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula>) at a very small liquid sample (less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.5 ~\mu \text{L}$ </tex-math></inline-formula>). The results of this investigation may provide new light on potential medicinal and sensor applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".