In vivo noninvasive visualization of retinal perfusion dysfunction in murine cerebral malaria by camera‐phone laser speckle imaging
Bibliographic record
Abstract
Cerebral malaria (CM) is a severe complication of Plasmodium falciparum infection associated with impaired cerebral blood flow. Visualization of the eye vasculature, which is embryologically derived from that of the brain, is used clinically to diagnose the syndrome. Here, we introduce camera-phone laser speckle imaging as a new tool for in vivo, noncontact two-dimensional mapping of blood flow dynamics in the experimental cerebral malaria (ECM) murine model of Plasmodium berghei ANKA. In a longitudinal study, we show that the camera-phone imager can detect an overall decrease in the retinal blood-flow-speed (BFS) as ECM develops in P. berghei ANKA infected mice, with no similar change observed in uninfected control mice or mice infected with a non-ECM inducing strain (P. berghei NK65). Furthermore, by analyzing relative alterations in the BFS of individual retinal vessels during the progression of ECM, we illustrate the strength of our imager in identifying different BFS-change heterogeneities in the retinas of ECM and uninfected mice. The technique creates new possibilities for objective investigations into the diagnosis and pathogenesis of CM noninvasively through the eye. The camera-phone laser speckle imager along with measured spatial blood perfusion maps of the retina of a mouse infected with P. berghei ANKA-a fatal ECM model-on different days during the progression of the infection (top, day 3 after infection; middle, day 5 after infection; and bottom, day 7 after infection).
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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 | 0.001 |
| 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 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".