The use of fluorescence enhancement to improve the microscopic diagnosis of falciparum malaria
Bibliographic record
Abstract
BACKGROUND: Giemsa staining of thick blood smears remains the "gold standard" for detecting malaria. However, this method is not very good for diagnosing low-level infections. A method for the simultaneous staining of Plasmodium-parasitized culture and blood smears for both bright field and fluorescence was developed and its ability to improve detection efficiency tested. METHODS: A total of 22 nucleic acid-specific fluorescent dyes were tested for their ability to provide easily observable staining of Plasmodium falciparum-parasitized red blood cells following Giemsa staining. RESULTS: Of the 14 dyes that demonstrated intense fluorescence staining, only SYBR Green 1, YOYO-1 and ethidum homodimer-2 could be detected using fluorescent microscopy, when cells were first stained with Giemsa. Giemsa staining was not effective when applied after the fluorescent dyes. SYBR Green 1 provided the best staining in the presence of Giemsa, as a very high percentage of the parasitized cells were simultaneously stained. When blood films were screened using fluorescence microscopy the parasites were more readily detectable due to the sharp contrast between the dark background and the specific, bright fluorescence produced by the parasites. CONCLUSION: The dual staining method reported here allows fluorescence staining, which enhances the reader's ability to detect parasites under low parasitaemia conditions, coupled with the ability to examine the same cell under bright field conditions to detect the characteristic morphology of Plasmodium species that is observed with Giemsa staining.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".