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/$\mu \text{L}$well below the level of clinical relevance (50–100 parasite/$\mu \text{L}$) at a very small liquid sample (less than$0.5 ~\mu \text{L}$). 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 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 | 0.000 | 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) | 0.000 | 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".